Learn TensorFlow, Google's open-source machine learning framework. Build and train neural networks, deploy models, and use TensorFlow Extended for production ML pipelines.
Deep Learning is the application of artificial neural networks to solve complex problems and commercial problems. There are several practical applications that have already been built using these techniques, such as: self-driving cars, development of new medicines, diagnosis of diseases, automatic generation of news, facial recognition, product recommendation, forecast of stock prices, and many others! The technique used to solve these problems is artificial neural networks, which aims to simulate how the human brain works. They are considered to be the most advanced techniques in the Machine Learning area.One of the most used libraries to implement this type of application is Google TensorFlow, which supports advanced architectures of artificial neural networks. There is also a repository called TensorFlow Hub which contains pre-trained neural networks for solving many kinds of problems, mainly in the area of Computer Vision and Natural Language Processing. The advantage is that you do not need to train a neural network from scratch! Google itself provides hundreds of ready-to-use models, so you just need to load and use them in your own projects. Another advantage is that few lines of code are needed to get the results!In this course you will have a practical overview of some of the main TensorFlow Hub models that can be applied to the development of Deep Learning projects! At the end, you will have all the necessary tools to use TensorFlow Hub to build complex solutions that can be applied to business problems. See below the projects that you are going to implement:Classification of five species of flowersDetection of over 80 different objectsCreating new images using style transferUse of GAN (generative adversarial network) to complete missing parts of imagesRecognition of actions in videosText polarity classification (positive and negative)Use o
Deep Learning is a hot topic today! This is because of the impact it's having in several industries. One of fields in which deep learning has the most influence today is Computer Vision.Object detection, Image Segmentation, Image Classification, Image Generation & People Counting To understand why Deep Learning based Computer Vision is so popular; it suffices to take a look at the different domains where giving a computer the power to understand its surroundings via a camera has changed our lives.Some applications of Computer Vision are:Helping doctors more efficiently carry out medical diagnosticsenabling farmers to harvest their products with robots, with the need for very little human intervention,Enable self-driving carsHelping quick response surveillance with smart CCTV systems, as the cameras now have an eye and a brainCreation of art with GANs, VAEs, and Diffusion ModelsData analytics in sports, where players' movements are monitored automatically using sophisticated computer vision algorithms.The demand for Computer Vision engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and Huggingface. We shall start by understanding how to build very simple mo
In this comprehensive course, you will master the fundamentals and advanced concepts of computer vision, focusing on Convolutional Neural Networks (CNN) and object detection models using TensorFlow and PyTorch. This course is designed to equip you with the skills required to build robust computer vision applications from scratch.What You Will LearnThroughout this course, you will gain expertise in:Introduction to Computer VisionUnderstanding image data and its structure.Exploring pixel values, channels, and color spaces.Learning about OpenCV for image manipulation and preprocessing.Deep Learning Fundamentals for Computer VisionIntroduction to Neural Networks and Deep Learning concepts.Understanding backpropagation and gradient descent.Key concepts like activation functions, loss functions, and optimization techniques.Convolutional Neural Networks (CNN)Introduction to CNN architecture and its components.Understanding convolution layers, pooling layers, and fully connected layers.Implementing CNN models using TensorFlow and PyTorch.Data Augmentation and PreprocessingTechniques for improving model performance through data augmentation.Using libraries like imgaug, Albumentations, and TensorFlow Data Pipeline.Transfer Learning for Computer VisionUtilizing pre-trained models such as ResNet, VGG, and EfficientNet.Fine-tuning and optimizing transfer learning models.Object Detection ModelsExploring object detection algorithms like:YOLO (You Only Look Once)Faster R-CNNImplement
Imagina crear, en pocos días, una inteligencia artificial que detecte tumores o enseñe a una consola Atari a batir récords, sin ser experto en matemáticas. El secreto está en proyectos guiados paso a paso, esto disparará tu motivación y retención.¿Qué vas a conseguir?Dominar Deep Learning e IA con TensorFlow desde cero, usando explicaciones que cualquier principiante puede entender a la primera.Construir 10 proyectos reales: detector de tumores, diagnóstico Covid con Transfer Learning, agente Atari autónomo, detector de violencia en vídeo y más, para impresionar a reclutadores con tu portafolio de proyectos de Inteligencia Artificial.Aprender con metodología 100 % práctica, probada para multiplicar la retención hasta 15 veces frente a clases teóricas con presentaciones aburridas.¿Por qué te importa?Empresas buscan talento en IA más que nunca: las vacantes que piden TensorFlow crecieron un 34 % en el último año y pagan hasta un 25 % más que la media STEM. Además, la tecnología de redes neuronales ya supera a radiólogos en ciertas tareas de diagnóstico, de modo que estas habilidades abren puertas que transforman carreras y cambian vidas.RequisitosSolo Python básico y ganas de experimentar—el resto (instalación de librerías, datasets y scripts) lo instalamos juntos en el curso
This new course is the updated version of the previous course Deep Learning for Computer Vision with Tensorflow2.X.It contains new classes explaining in detail many state of the art algorithms for image classification and object detection.The course was entirely written using Google Colaboratory(Colab) in order to help students that don't have a GPU card in your local system, however you can follow the course easily if you have one.This time the course starts explaining in detail the building blocks from ConvNets which are the base for image classification and the base for the feature extractors in the latest object detection algorithms.We're going to study in detail the following concepts and algorithms:- Image Fundamentals in Computer Vision,- Load images in Generators with TensorFlow,- Convolution Operation,- Sparsity Connections and parameter sharing,- Depthwise separable convolution,- Padding,- Conv2D layer with Tensorflow,- Pooling layer,- Fully connected layer,- Batch Normalization,- ReLU activation and other functions,- Number of training parameters calculation,- Image Augmentation, etc- Different ConvNets architectures such as: * LeNet5, * AlexNet, * VGG-16, * ResNet, * Inception, * The lastest state of art Vision Transformer (ViT)- Many practical applications using famous datasets and sources such as: * Covid19 on X-Ray images, * CIFAR10, * Fashion MNIST, * BCCD, * COCO dataset, * Open Images Dataset V6 through Voxel Fifty
THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TENSORFLOW & KERAS IN PYTHON!It is a full 7-Hour Python Tensorflow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using two of the most important Deep Learning frameworks- Tensorflow and Keras. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the Tensorflow & Keras framework in Python.. This means, this course covers the important aspects of Keras and Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow and Keras based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow and Keras is revolutionizing Deep Learning... By gaining proficiency in Keras and and Tensorflow, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TENSORFLOW BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably wit
Atenção! Nas aulas deste curso é utilizada a versão 1.x do TensorFlow, sendo possível acompanhar as aulas utilizando essa versão. Adicionalmente, disponibilizamos o código atualizado considerando a versão 2.x. Em breve pretendemos regravar todas as aulas deste cursoA área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, eBay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o
Complete Tensorflow Mastery For Machine Learning & Deep Learning in PythonTHIS IS A COMPLETE DATA SCIENCE TRAINING WITH TENSORFLOW IN PYTHON!It is a full 7-Hour Python Tensorflow Data Science Boot Camp that will help you learn statistical modelling, data visualization, machine learning and basic deep learning using the Tensorflow framework in Python.. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical data science using the Tensorflow framework in Python.. This means, this course covers all the aspects of practical data science with Tensorflow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python Tensorflow based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of Tensorflow is revolutionizing Deep Learning... By storing, filtering, managing, and manipulating data in Python and Tensorflow, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL PYTHON TENSORFLOW BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journa
This course provides you to be able to build Deep Neural Networks models for different business domains with one of the most common machine learning library TensorFlow provided by Google AI team. The both concept of deep learning and its applications will be mentioned in this course. Also, we will focus on Keras. We will also focus on the advanced topics in this lecture such as transfer learning, autoencoders, face recognition (including those models: VGG-Face, Google FaceNet, OpenFace and Facebook DeepFace).This course appeals to ones who interested in Machine Learning, Data Science and AI. Also, you don't have to be attend any ML course before.
Learn Complete Machine Learning & Data Science Bootcamp 2025
Avec l'avènement des intelligences artificielles comme ChatGPT et Midjourney, nous vivons une véritable révolution dans le monde de la technologie. Et il est devenu indispensable de posséder des compétences en intelligence artificielle pour rester compétitif sur le marché de l'emploi. Si vous cherchez à développer vos compétences en IA, ce cours est exactement ce dont vous avez besoin pour acquérir les bases nécessaires et vous positionner comme un expert dans ce domaine en pleine croissance.Pourquoi Le deep learning avec Tensorflow et non Pytorch ?Parce que :TensorFlow a été créé par Google en 2015, tandis que PyTorch est apparu en 2017. TensorFlow a donc été utilisé et testé plus longtemps dans des applications de production.TensorFlow est plus adapté aux projets de grande envergure. TensorFlow a été conçu pour être utilisé sur des clusters de machines, ce qui en fait un choix plus approprié pour les projets de grande envergure.TensorFlow offre une grande flexibilité en termes de déploiement. TensorFlow peut être utilisé pour déployer des modèles sur différents types d'appareils, y compris les ordinateurs, les serveurs, les téléphones mobiles et les dispositifs de l'internet des objets.TensorFlow dispose d'un écosystème plus large et est utilisé dans un large éventail d'applications, allant de la reconnaissance d'image et de la vision par ordinateur à la prédiction de séries temporelles et à la modélisation du langage naturel.Les bases mathématiques du Deep Learning : Pas besoin d’être un matheuxCependant, Tensorflow encapsule plusieurs concepts mathématiques de base dont la compréhension est indispensable pour bien entrainer des réseaux de neurones.C’est pourquoi nous débutons cette formation par les bases mathématiques du Deep Learning, mais de façon pratique avec du code et non des formules mathématiques.Si vous avez le niveau Lycée en Mathématique mais pense
Just launched with all modern best practices for building neural networks with TensorFlow and becoming a TensorFlow & Deep Learning Expert!Join a live online community of over 900,000+ students and a course taught by a TensorFlow expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks.TensorFlow experts earn up to $204,000 USD a year, with the average salary hovering around $148,000 USD. By taking this course you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow Developer!Here is a full course breakdown of everything we will teach (yes, it's very comprehensive, but don't be intimidated, as we will teach you everything from scratch!):The goal of this course is to teach you all the skills necessary for you to become a top 10% TensorFlow Developer.This course will be very hands on and project based. You won't just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter. 0 — TensorFlow FundamentalsIntroduction to tensors (creating tensors)Getting information from tensors (tensor attributes)Manipulating tensors (tensor operations)Tensors and NumPyUsing @tf.function (a way to speed up your regular Python functions)Using GPUs with TensorFlow1 — Neural Network Regression with TensorFlowBuild TensorFlow sequential models with multiple layersPrepare data for use with a machine learning model
Python, Java, PyCharm, Android Studio and MNIST. Learn to code and build apps! Use machine learning models in hands-on projects. A wildly successful Kickstarter funded this courseExplore machine learning concepts. Learn how to use TensorFlow 1.4.1 to build, train, and test machine learning models. We explore Python 3.6.2 and Java 8 languages, and how to use PyCharm 2017.2.3 and Android Studio 3 to build apps. A machine learning framework for everyoneIf you want to build sophisticated and intelligent mobile apps or simply want to know more about how machine learning works in a mobile environment, this course is for you.Be one of the firstThere are next to no courses on big platforms that focus on mobile machine learning in particular. All of them focus specifically on machine learning for a desktop or laptop environment.We provide clear, concise explanations at each step along the way so that viewers can not only replicate, but also understand and expand upon what I teach. Other courses don’t do a great job of explaining exactly what is going on at each step in the process and why we choose to build models the way we do. No prior knowledge is requiredWe will teach you all you need to know about the languages, software and technologies we use. If you have lots of experience building machine learning apps, you may find this course a little slow because it’s designed for beginners.Jump into a field that has more demand than supplyMachine learning changes everything. It’s bringing us self-driving cars, facial recognition and artificial intelligence. And the best part is: anyone can create such innovations."This course is GREA
Welcome to the Complete Guide to TensorFlow for Deep Learning with Python! This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand. Other courses and tutorials have tended to stay away from pure tensorflow and instead use abstractions that give the user less control. Here we present a course that finally serves as a complete guide to using the TensorFlow framework as intended, while showing you the latest techniques available in deep learning! This course is designed to balance theory and practical implementation, with complete jupyter notebook guides of code and easy to reference slides and notes. We also have plenty of exercises to test your new skills along the way! This course covers a variety of topics, including Neural Network BasicsTensorFlow BasicsArtificial Neural NetworksDensely Connected NetworksConvolutional Neural NetworksRecurrent Neural NetworksAutoEncodersReinforcement LearningOpenAI Gymand much more! There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system
TensorFlow is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. Like TensorFlow, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It's also modular, and that makes debugging your code a breeze. If you’re someone who wants to get hands-on with Deep Learning by building and training Neural Networks, then go for this course.This course takes a step-by-step approach where every topic is explicated with the help of a real-world examples. You will begin with learning some of the Deep Learning algorithms with TensorFlow such as Convolutional Neural Networks and Deep Reinforcement Learning algorithms such as Deep Q Networks and Asynchronous Advantage Actor-Critic. You will then explore Deep Reinforcement Learning algorithms in-depth with real-world datasets to get a hands-on understanding of neural network programming and Autoencoder applications. You will also predict business decisions with NLP wherein you will learn how to program a machine to identify a human face, predict stock market prices, and process text as part of Natural Language Processing (NLP). Next, you will explore the imperative side of PyTorch for dynamic neural network programming. Finally, you will build two mini-projects, first focusing on applying dynamic neural networks to image recognition and second NLP-oriented problems (grammar parsing).By the end of this course, you will have a complete understanding of the essential ML libraries TensorFlow and PyTorch for developing and training neural networks of varying complexities, without any hassle.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Roland Meertens is currently developing computer vision algorithms for self-driving ca
Machine learning has become one of the most common practices used by many organizations, groups and individuals. It helps various software to predict the outcome more precisely without any programming. Machine learning finds the pattern in the input data and uses statistical analysis to foretell the result. To support its extensive requirements, Tensorflow was launched by Google. In order to provide next-generation machine learning solutions, we have hand-picked this course covering all its aspects. Why this course is important? Machine learning often requires heavy computation and for that Tensorflow was developed as an open source library. Tensorflow not only does the heavy computation but can also build dataflows. Apart from machine learning, it is also used in wide variety of other domains by the experts. This course contains different topics to make you understand everything about next-generation machine learning by Tensorflow. What makes this course so valuable? It includes all the basics of Tensorflow with detail description of tensors, operators and variables. Installation of Tensorflow on Windows, Mac and Linux is clearly shown. Additionally, it gives insights into the basics of machine learning and its types. This course also covers various algorithms like linear regression, logistic regression, NN regression, K-Means algorithm and others. Herein, advanced machine learning is also well elaborated with the topics of neural networks, convolution neural networks, recurrent neural networks and so on. This course includes- 1.Tensorflow fundamentals and installation 2. Details about tensors, operators, variables and others 3. Details about machine learning, inference and its types 4. Different algorithms like linear regression, logistic regression, clustering, K-means algorithm, kernels and many more 5. Various advanced learning networks and its implementation - Neural Networks, Conv
Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don't take the beginners into consideration :(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, and built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression models for car price prediction, text classifiers for movie reviews, binary classifiers for malaria prediction) using Tensorflow and Huggingface transformers, to more advanced models (like object detection models with YOLO, lyrics generator model with GPT2 and Image generation with GANs)After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep-learning solutions that big tech companies encounter.You will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Cla
Learn What is YOLO algorithm? | Deep Learning Tutorial 31 (Tensorflow, Keras & Python)
If you are interested in Machine Learning, Neural Networks, Deep Learning, Deep Neural Networks (DNN), and Convolution Neural Networks (CNN) with an in-depth and clear understanding, then this course is for you.Topics are explained in detail. Concepts are developed progressively in a step by step manner. I sometimes spent more than 10 minutes discussing a single slide instead of rushing through it. This should help you to be in sync with the material presented and help you better understand it.The hands-on examples are selected primarily to make you familiar with some aspects of TensorFlow 2 or other skills that may be very useful if you need to run a large and complex neural network job of your own in the future.Hand-on examples are available for you to download.Please watch the first two videos to have a better understanding of the course.TOPICS COVEREDWhat is Machine Learning?Linear RegressionSteps to Calculate the ParametersLinear Regression-Gradient Descent using Mean Squared Error (MSE) Cost FunctionLogistic Regression: ClassificationDecision BoundarySigmoid FunctionNon-Linear Decision BoundaryLogistic Regression: Gradient DescentGradient Descent using Mean Squared Error Cost FunctionProblems with MSE Cost Function for Logistic RegressionIn Search for an Alternative Cost-FunctionEntropy and Cross-EntropyCross-Entropy: Cost Function for Logistic RegressionGradient Descent with Cross Entropy Cost FunctionLogistic Regression: Multiclass ClassificationIntroduction to Neural NetworkLogical OperatorsModeling Logical Operators using Perceptron(s)Logical Operators using Combination of Perceptron
Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!This course is designed for ML practitioners who want to enhance their skills and move up the ladder with Deep Learning!This course is made to give you all the required knowledge at the beginning of your journey so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips, and tricks you would require to work in the Deep Learning space.It gives a detailed guide on Tensorflow and Keras along with in-depth knowledge of Deep Learning algorithms. All the algorithms are covered in detail so that the learner gains a good understanding of the concepts. One needs to have a clear understanding of what goes behind the scenes to convert a good model to a great model. This course will enable you to develop complex deep-learning architectures with ease and improve your model performance with several tips and tricks.Deep Learning Algorithms Covered:1. Feed Forward Networks (FFN)2. Convolutional Neural Networks (CNN)3. Recurring Neural Networks (RNN)4. Long Short-Term Memory Networks (LSTMs)5. Gated Recurrent Unit (GRU)6. Autoencoders7. Transfer Learning8. Generative Adversarial Networks (GANs)Our exotic journey will include the concepts of:1. The most important concepts of Tensorflow and Keras from very basic.2. The two ways of model building i.e. Sequential and Functional API.3. All the building blocks of Deep Learning models are explained in detail to enable students to make decisions while training their model and improving model performance.4. Hands-on learning of Deep Learning algorithms from the beginner
Master TensorFlow 2 and Keras. ANNs, CNNs, RNNs, GANs, deployment.
The first course in this specialization is an excellent entry point for practical computer vision. You will learn to build and deploy models in the browser using TensorFlow.js, including creating a real-time object detector that runs on a webcam feed.
Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. So if you’re looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path. Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Setting up TensorFlow for actual industrial use, including high-performance setup aspects like multi-GPU support Embedded with solid projects and examples to teach you how to implement TensorFlow in production Empower you to go from concept to a production-ready machine learning setup/pipeline capable of real-world usage Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with TensorBoard – all within an example-rich context using problems from multiple industries. The focus is towards introducing new concepts through problems which are coded and solved over the course of each video. You will then learn how to implement TensorFlow in production. Each project in this Learning Path provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with tensors. Finally, you will be acquainted with the different paradigms of performing deep learning such as deep neural nets, convolutional neural networks, recurrent neural networks, and more,
Bienvenido a este curso 100% práctico y aplicado en el que podrás aprender de forma intuitiva, guiada y paso-a-paso el despliegue de modelos Deep Learning para ambientes de Desarrollo y principalmente para Producción en escenarios de alto desempeño usando la librería TensorFlow 2.0 y la creación de servicios REST.Estructura temática:¿Por qué desplegar modelos Deep Learning?Despliegue en Desarrollo vs ProducciónServidores de despliegue en la Nube: Google Cloud Platform (GCP) y CentOSDespliegue de modelos en la nube como Servicio Web REST desde cero (APIs)Gestor de contenedores Docker para despliegues en Producción (Docker Swarm, TensorFlow Serving) Implementación de llamadas al Servicio Web desde ceroConsideraciones técnicas para el despliegue de modelos Deep LearningDevOps y Machine Learning / MLOps | IAOps | XXOpsInteroperabilidad de modelos: ONNX.Despliegue Customizado vs Plataformas100% práctico:El curso prioriza el desarrollo de algoritmos en sesiones de laboratorio y actividades de programación 100% hands-on con los que podrás reproducir cada una de las líneas de código con explicaciones muy bien detalladas, sin descuidar los fundamentos teóricos de cada uno de los conceptos descritos.Herramientas:Todas las herramientas necesarias para el curso se podrán configurar directamente en la nube de Google; por tanto, no será necesario invertir tiempo en instalaciones de herramienta de forma local.El curso se desarrolla con las herramientas más populares y de alta madurez del ecosistema de Python 3.0 como:TensorFlow 2.0TensorFlow ServingFlask
Learn Deep Learning: Getting Started
Learn TensorFlow 2.0 and Keras for deep learning. Build neural networks for computer vision, NLP, and time series prediction.
Welcome to the Deep Learning Fundamentals course on Udemy! Are you ready to unlock the power of neural networks and delve into the exciting world of artificial intelligence? Look no further! This comprehensive course is designed to equip you with the essential knowledge and practical skills needed to become proficient in both Tensorflow and Pytorch based deep learning together!Deep learning has revolutionized the field of AI, enabling machines to learn from vast amounts of data and make accurate predictions, recognize patterns, and perform complex tasks. In this course, we will demystify the concepts behind deep learning and guide you through hands-on exercises to build and train your neural networks.Here's an overview of what you'll learn:Introduction to Deep Learning:Understand the fundamentals of artificial neural networks.Explore the history and evolution of deep learning.Gain insights into real-world applications and their impact.Neural Networks and Architectures:Study the structure and functioning of artificial neurons.Learn about various neural network architectures, including feedforward, convolutional, and recurrent networks.Explore activation functions, weight initialization, and regularization techniques.Building Deep Learning Models:Implement deep learning models using popular frameworks such as TensorFlow or PyTorch.Understand the process of data preprocessing, including feature scaling and one-hot encoding.Design effective training and validation sets for model evaluation.Training Neural Networks:Grasp the concept of backpropagation and how it enables model training.Explore optimization algorithms like stochastic gradient descent (SGD) and Adam.Learn techniques to prevent overfitting, such as dropout and ea
Welcome to the "Deep Learning Neural Networks with TensorFlow" course! This comprehensive program is designed to equip you with the essential knowledge and hands-on skills required to navigate the exciting field of deep learning using TensorFlow.Overview: In this course, you will embark on a journey through the fundamentals and advanced concepts of deep learning neural networks. We'll start by providing you with a solid foundation, introducing the core principles of neural networks, including the scenario of Perceptron and the creation of neural networks using TensorFlow.Hands-on Projects: To enhance your learning experience, we have incorporated practical projects that allow you to apply your theoretical knowledge to real-world scenarios. The "Face Mask Detection Application" project in Section 2 and the "Implementing Linear Model with Python" project in Section 3 will provide you with valuable hands-on experience, reinforcing your understanding of TensorFlow.Advanced Applications: Our course goes beyond the basics, delving into advanced applications of deep learning. Section 4 explores the fascinating realm of automatic image captioning for social media using TensorFlow. You will learn to preprocess data, define complex models, and deploy applications, gaining practical insights into the cutting-edge capabilities of deep learning.Why TensorFlow? TensorFlow is a leading open-source deep learning framework, widely adopted for its flexibility, scalability, and extensive community support. Whether you're a beginner or an experienced professional, this course caters to learners of all levels, guiding you through the intricacies of deep learning with TensorFlow.Get ready to unravel the mysteries of neural networks, develop practical skills, and unleash the power of TensorFlow in the dynamic field of deep learning. Join us on this exciting learning journey, and let's dive deep into the
Machine learning allows you to build more powerful, more accurate and more user friendly software that can better respond and adapt.Many companies are integrating machine learning or have already done so, including the biggest Google, Facebook, Netflix, and Amazon.There are many high paying machine learning jobs.Jump into this fun and exciting course to land your next interesting and high paying job with the projects you’ll build and problems you’ll learn how to solve.In just a matter of hours you'll have new skills with projects to back them up: Deep dive into machine learningProblems that machine learning solvesTypes of machine learningCommon machine learning structuresSteps to building a machine learning modelBuild a linear regression machine learning model with TensorFlowTest and train the modelPython variables and operatorsCollection typesConditionals and loopsFunctionsClasses and objectsInstall and import NumPyBuild NumPy arraysMultidimensional NumPy arraysArray indexes and propertiesNumPy functionsNumPy operationsAnd much more!Add new skills to your resume in this project based course: Graph data with PyPlotCustomize graphsBuild 3D graphs with PyPlotUse TensorFlow to build a program to categorize irises into different species.Build a classification modelTrack dataImplement logicImplement responsivenessBuild data structuresReplace Python lists with NumPy arraysBuild and use NumPy arraysUse common array
Course ContentsDeep Learning and revolutionized Artificial Intelligence and data science. Deep Learning teaches computers to process data in a way that is inspired by the human brain.This is complete and comprehensive course on deep learning. This course covers the theory and intuition behind deep learning models and then implementing all the deep learning models both in Pytorch and Tensor flow.Practical Oriented explanations Deep Learning Models with implementation both in Pytorch and Tensor Flow.No need of any prerequisites. I will teach you everything from scratch.Job Oriented StructureSections of the Course· Introduction of the Course· Introduction to Google Colab· Python Crash Course· Data Preprocessing· Regression Analysis· Logistic Regression· Introduction to Neural Networks and Deep Learning· Activation Functions· Loss Functions· Back Propagation· Neural Networks for Regression Analysis· Neural Networks for Classification· Dropout Regularization and Batch Normalization· Optimizers· Adding Custom Loss Function and Custom Layers to Neural Networks· Convolutional Neural Network (CNN)· One Dimensional CNN· Setting Early Stopping Criterion in CNN· Recurrent Neural Network (RNN)· Long Short-Term Memory (LSTM) Network· Bidirectional LSTM· Generative Adversarial Network (GAN)· DCGANs· Autoencoders· LSTM Autoencoders· Variational Autoencoders· Neural Style Transfer· Transformers· Vision Transformer· Time Series Transformers. K-means Clustering. Principle Component Analysis. Deep Learning Models with implementation both in Pytorch and Tensor Flow.
Deep learning is the intersection of statistics, artificial intelligence, and data to build accurate models. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Tensorflow is Google’s popular offering for machine learning and deep learning. It has become a popular choice of tool for performing fast, efficient, and accurate deep learning. TensorFlow is one of the most comprehensive libraries for implementing deep learning.This comprehensive 2-in-1 course is your step-by-step guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data with the help of insightful examples that you can relate to and show how these can be exploited in the real world with complex raw data. You will also learn how to scale and deploy your deep learning models on the cloud using tools and frameworks such as asTensorFlow, Keras, and Google Cloud MLE. This learning path presents the implementation of practical, real-world projects, teaching you how to leverage TensorFlow’s capabilities to perform efficient deep learning.This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-on Deep Learning with TensorFlow, is designed to help you overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. You will then learn deep neural networks for different problems and explore the applications of convolutional neural networks on two real datasets. You will also learn how autoencoders can be used for efficient data representation. Finally, you will understand some of the important techniques to implement generative adversarial networks.The second course,
Das sagen Teilnehmer über diesen Kurs:"Sehr aktiver Dozent der sich um die Kursteilnehmer und den Kurs kümmert. Der Tensorflow Kurs hat viele beispiele was mir geholfen hat Tensorflow und Keras besser zu verstehen. Ebenfalls sehr gut waren auch die Begriff erklärungen die einem sehr helfen ML als beginner zu lernen." - Ibrahim Akkulak"Ich würde den Kurs auf jeden Fall weiter empfehlen. Mehr Content als gedacht und sehr viele Erklärungen. Top!" - Erik Andrè Thürsam"Der Kurs gefällt mir ganz gut und bringt viele Beispiele ein. Der Saif beantwortet Fragen super schnell und ist sehr hilfsbereit. Empfehle den Kurs sehr für alle die Deep Learning mit vielen Praxisbeispielen lernen möchten." - Simon BehrensDeep Learning ist eines der angesagtesten Themen weit und breit. Insbesondere wird Deep Learning und Künstliche Neuronale Netze in vielen Technologien in deinem Umfeld eingesetzt, um dir ein noch angenehmeres Leben zu ermöglichen. Mithilfe diesen Praxis-Kurs bringe ich dir bei wie man Deep Learning mithilfe von Keras, Tensorflow und Python einsetzt. Du wirst eine gute Mischung von Theorie und Praxis in diesen Kurs erhalten. Viele der Techniken werden anhand von echten Praxis Projekte dir vermittelt. Warum solltest du Keras lernen? Keras wird von den "Big Five" Unternehmen wie Apple, Google, Facebook, Amazon und Microsoft in vielen ihrer Produkte eingesetzt, um Machine Learning noch effizienter zu nutzen! Ebenfalls werde ich ihn auch immer auf dem neusten Stand der Technik und Wissenschaft halten. Lerne wie du Keras meisterst und schreibe dich JETZT ein!
This course provides an introduction to TensorFlow, one of the most popular deep learning frameworks. You'll learn the basics of building and training neural networks for computer vision tasks.
Ever wondered how AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion really work? In this course, you will learn the foundations of these groundbreaking applications.Welcome to Tensorflow 2.0!What an exciting time. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.Tensorflow is Google's library for deep learning and artificial intelligence.Deep Learning has been responsible for some amazing achievements recently, such as:Generating beautiful, photo-realistic images of people and things that never existed (GANs)Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)Self-driving cars (Computer Vision)Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning)Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.In other words, if you want to do deep learning, you gotta know Tensorflow.This course is for beginner-level students all the way up to expert-level students. How can this be?If you've just taken my free Numpy prerequisite, then you know everything you need to jump right in. We will start with some very basic machine learning models and advance to state of the art concepts.Along the way, you will learn about
This Course simplifies the advanced Deep Learning concepts like Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short Term Memory (LSTM), Gated Recurrent Units(GRU), etc. TensorFlow, Keras, Google Colab, Real World Projects and Case Studies on topics like Regression and Classification have been described in great detail. Advanced Case studies like Self Driving Cars will be discussed in great detail. Currently the course has few case studies.The objective is to include at least 20 real world projects soon. Case studies on topics like Object detection will also be included. TensorFlow and Keras basics and advanced concepts have been discussed in great detail. The ultimate goal of this course is to make the learner able to solve real world problems using deep learning. After completion of this course the Learner shall also be able to pass the Google TensorFlow Certification Examination which is one of the prestigious Certification. Learner will also get the certificate of completion from Udemy after completing the Course. After taking this course the learner will be expert in following topics. a) Theoretical Deep Learning Concepts.b) Convolutional Neural Networksc) Long-short term memoryd) Generative Adversarial Networkse) Encoder- Decoder Modelsf) Attention Modelsg) Object detectionh) Image Segmentationi) Transfer Learningj) Open CV using Pythonk) Building and deploying Deep Neural Networks l) Professional Google Tensor Flow developer m) Using Google Colab for writing Deep Learning coden) Python programming for Deep Neural NetworksThe Learners are advised to practice the Tensor Flow code as they watch the videos on Programming from this course. First Few sections have been uploaded, The course is in updation phase and the remaining sections will be added soon.
Welcome to our Python & TensorFlow for Machine Learning complete course. This intensive program is designed for both beginners eager to dive into the world of data science and seasoned professionals looking to deepen their understanding of machine learning, deep learning, and TensorFlow's capabilities.Starting with Python—a cornerstone of modern AI development—we'll guide you through its essential features and libraries that make data manipulation and analysis a breeze. As we delve into machine learning, you'll learn the foundational algorithms and techniques, moving seamlessly from supervised to unsupervised learning, paving the way for the magic of deep learning.With TensorFlow, one of the most dynamic and widely-used deep learning frameworks, we'll uncover how to craft sophisticated neural network architectures, optimize models, and deploy AI-powered solutions. We don't just want you to learn—we aim for you to master. By the course's end, you'll not only grasp the theories but also gain hands-on experience, ensuring that you're industry-ready.Whether you aspire to innovate in AI research or implement solutions in business settings, this comprehensive course promises a profound understanding, equipping you with the tools and knowledge to harness the power of Python, Machine Learning, and TensorFlow.We're excited about this journey, and we hope to see you inside!
This course is complete guide of AWS SageMaker wherein student will learn how to build, deploy SageMaker models by brining on-premises docker container and integrate it to SageMaker. Course will also do deep drive on how to bring your own algorithms in AWS SageMaker Environment. Course will also explain how to use pre-built optimized SageMaker Algorithm.Course will also do deep drive how to create pipeline and workflow so model could be retrained and scheduled automatically.This course covers all aspect of AWS Certified Machine Learning Specialty (MLS-C01)This course will give you fair ideas of how to build Transformer framework in Keras for multi class classification use cases. Another way of solving multi class classification by using pre-trained model like Bert .Both the Deep learning model later encapsulated in Docker in local machine and then later push back to AWS ECR repository.This course offers:AWS Certified Machine Learning Specialty (MLS-C01)What is SageMaker and why it is requiredSageMaker Machine Learning lifecycleSageMaker ArchitectureSageMaker training techniques:Bring your own docker container from on premise to SageMakerBring your own algorithms from local machine to SageMakerSageMaker Pre built AlgorithmSageMaker Pipeline developmentSchedule the SageMaker Training notebookMore than 5 hour course are provided which helps beginners to excel in SageMaker and will be well versed with build, train and deploy the models in SageMaker
This comprehensive course will take you on a journey from the foundational concepts of machine learning and TensorFlow to the creation of advanced, real world deep learning models. I'll start with the basics, giving you a solid understanding of how neural networks work, and progressively build up your skills to tackle complex problems in computer vision, natural language processing (NLP), and more. Through a series of hands-on labs, projects, and practical examples, you'll learn to not only build and train models but also to understand the "why" behind the code, enabling you to confidently solve new and challenging problems.This course is designed for anyone with a basic understanding of Python programming who wants to build a career in machine learning and artificial intelligence. Whether you're a student, a software developer, or a data analyst, this course will provide you with the practical skills and foundational knowledge to become a proficient TensorFlow practitioner.Why Take This Course?Artificial Intelligence is transforming industries worldwide, and deep learning lies at its core. TensorFlow, developed by Google, has become the industry standard library for building and deploying AI applications at scale. This course provides a step by step learning journey, blending theory with hands-on coding so you not only understand concepts but can also implement them in real world projects.By the end of this course, you’ll have the knowledge and confidence to:Understand the foundations of deep learning and TensorFlow.Build simple and complex neural networks from scratch.Train, evaluate, and optimize models using modern techniques.Work with Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and advanced architectures.Deploy machine learning models in real-world scenarios.What You’ll L
Do you want to learn about Web Development and Machine learning at the same time? With this course you can do exactly that and more!This course was funded by a wildly successful KickstarterWith the Deep Learning of Angular 2 and Tensorflow, You will learn about Javascript frameworks for creating websites and create Apps driven by Machine Learning by learning Tensorflow as well as PyCharm, Python, Android Studio and more!About Tensorflow: We use frameworks like TensorFlow that make it easy to build, train, test, and use machine learning models. TensorFlow makes machine learning so much more accessible to programmers everywhereYou can expect a complete and comprehensive course that guides you first through the basics, then through some simple models. You will end up with a portfolio of apps driven by machine learning, as well as the know-how to create more and expand upon what we build together.About Angular 2: JavaScript is one of the fundamental languages of the web. JavaScript is easy to program in but some tasks are difficult. JavaScript frameworks are built to make these difficult tasks easier. In this course you will learn how to code with Angular.js 2, a powerful framework that makes building web apps a breeze. In this course you will learn web programming fundamentals and other valuable skill boosting career knowledge.This course is project based so you will not be learning a bunch of useless coding practices. At the end of this course you will have real world apps to use in your portfolio. We feel that project based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately.Also, now included in this course are bonus courses of other related topics, such as C# and Java! You get more content at a great price!En
Are you short on time to start from scratch to use deep learning to solve complex problems involving topics like neural networks and reinforcement learning? Than this course is for you!This course is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow. You will begin with a quick introduction to TensorFlow essentials. Next, you start with deep neural networks for different problems and also explore the applications of Convolutional Neural Networks on two real datasets. We will than walk you through different approaches to RL. You’ll move from a simple Q-learning to a more complex, deep RL architecture and implement your algorithms using Tensorflow’s Python API. You’ll be training your agents on two different games in a number of complex scenarios to make them more intelligent and perceptive.By the end of this course, you’ll be able to implement RL-based solutions in your projects from scratch using Tensorflow and Python. Also you will be able to develop deep learning based solutions to any kind of problem you have, without any need to learn deep learning models from scratch, rather using tensorflow and it’s enormous power.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-on Deep Learning with TensorFlow is designed to help you to overcome various data science problems by using efficient deep learning models built in TensorFlow.The course begins with a quick introduction to TensorFlow essentials. Next, we start with deep neural networks for different problems and then explore the applications of Convolutional Neural Networks on two real datasets. If you’re facing time series problem then we will show you how to tackle it using RNN. We will also highlight how autoencoders can be used for efficient data representation. Lastly, we will take you through some of th
This course teaches machine learning from the basics so that you can get started with created amazing machine learning programs. With a well structured architecture, this course is divided into 4 modules:Theory section: It is very important to understand the reason of learning something. The need for learning machine learning and javascript in this particular case is explained in this section.Foundation section: In this section, most of the basic topics required to approach a particular problem are covered like the basics of javascript, what are neural networks, dom manipulation, what are tensors and many more such topicsPractice section: In this section, you put your learnt skills to a test by writing code to solve a particular problem. The explanation of the solution to the problem is also provided in good detail which makes hands-on learning even more efficient. Project section: In this section, we build together a full stack project which has some real life use case and can provide a glimpse on the value creation by writing good quality machine learning programsHappy Coding,Vinay Phadnis :)
TensorFlow is the world’s most widely adopted framework for Machine Learning and Deep Learning. TensorFlow 2.0 is a major milestone due to its inclusion of some major changes making TensorFlow easier to learn and use such as “Eager Execution”. It will support more platforms and languages, improved compatibility and remove deprecated APIs.This course will guide you to upgrade your skills in Machine Learning by practically applying them by building real-world Machine Learning projects.Each section should cover a specific project on a Machine Learning task and you will learn how to implement it into your system using TensorFlow 2. You will implement various Machine Learning techniques and algorithms using the TensorFlow 2 library. Each project will put your skills to test, help you understand and overcome the challenges you can face in a real-world scenario and provide some tips and tricks to help you become more efficient. Throughout the course, you will cover the new features of TensorFlow 2 such as Eager Execution. You will cover at least 3-4 projects. You will also cover some tasks such as Reinforcement Learning and Transfer Learning.By the end of the course, you will be confident to build your own Machine Learning Systems with TensorFlow 2 and will be able to add this valuable skill to your CV.About the AuthorVlad Ionescu is a lecturer at Babes-Bolyai University. He has a PhD in machine learning, a field he is continuously researching and exploring every day with technologies such as Python, Keras, and TensorFlow.His philosophy is “If I can't explain something well enough for most people to understand it, I need to go back and understand it better myself before trying again”. This philosophy helps him to give of his best in his lectures and tutorials.He started as a high school computer science teacher while he was doing his Masters over 5 years ago. Right now, he teaches various university-level courses and tutorials, coverin
A guided project that teaches the fundamentals of using TensorFlow for image classification. You will build, train, and evaluate a neural network to classify images from the CIFAR-10 dataset.
Deep Learning with TensorFlow focuses on building and deploying advanced neural network models that mimic the human brain’s learning capabilities to solve complex problems. This topic explains the architecture of deep neural networks, including layers, neurons, activation functions, loss functions, backpropagation, and optimization techniques. Learners explore how TensorFlow, a leading open-source framework, enables the design, training, and deployment of deep learning models efficiently, handling large datasets and computational requirements. Practical applications such as image classification, object detection, natural language processing, speech recognition, and recommendation systems are highlighted to show real-world relevance. The topic also covers hyperparameter tuning, model evaluation, performance optimization, and techniques to prevent overfitting or underfitting. Learners gain a comprehensive understanding of how to preprocess data, structure neural networks, and apply advanced algorithms to achieve accurate and reliable results. This topic is ideal for students, AI enthusiasts, developers, and data scientists seeking practical deep learning expertise. By mastering Deep Learning with TensorFlow, learners develop the skills necessary to build intelligent systems that solve complex problems, contribute to innovation in AI-driven industries, and prepare for advanced roles in artificial intelligence, data science, and machine learning engineering. The knowledge gained empowers learners to create scalable, high-performing AI solutions that can be applied across multiple sectors, from technology to business intelligence.
In this intensive one-hour course, you’ll dive headfirst into the world of machine learning using TensorFlow and Google Colab. No pit stops—just pure acceleration!What You’ll Cover:TensorFlow Basics: Understand the core concepts, from defining layers to training models.Google Colab Mastery: Leverage Colab’s cloud-based environment for seamless development.Data Prep Express: Quickly preprocess your data without detours.Model Construction: Design and build neural networks like a seasoned pro.Training and Evaluation: Witness your model learn, iterate, and fine-tune for optimal performance.Why Take This Course?Speedy Results: Get up to speed in just one hour.Practical Skills: Apply what you learn to real-world problems.No Pit Stops: We’re all about efficiency here!Prerequisites:Basic Python knowledge (if you can write a for loop, you’re set!)Curiosity and a dash of determinationReady to accelerate your ML journey? Buckle up!Whether you’re a data enthusiast, a developer, or a curious learner, this course is your express ticket to mastering machine learning essentials. Let’s hit the road! Your course instructor is me Adam Cole, a professional software engineer with 5 years working on enterprise level applications. Feel free to send me any questions on LinkedIn at Adam Cole Adam Cole BSc MBCS.
Este curso básico de TensorFlow te enseñará a crear redes neuronales para Deep Learning o aprendizaje profundo.Es una guía fácil con muchos ejemplos, para entener las complejidades del marco de TensorFlow de Google.Este curso está repleto de ejemplos escritos en Python sobre Jupyter Notebook, para que puedas probarlos tu mismo.Estos son los temas tratados en este curso de TensorFlow :- Introduccion al Machine Learning- Instalacion del entorno de trabajo- Curso básico de Python sobre las librerías usadas en este curso: - NumPy - Pandas - Matplotlib - SciKit Learn- Introducción a las redes neuronales (Deep Learning) - Neuronas y perceptrones - Funciones de activacion - Funciones de coste - Algoritmo del gradiente descendiente - Practicar con una red neuronal en el navegador- TensorFlow - Introducción a TensorFlow - Sintaxis básica de TensorFlow - Grafos en TensorFlow - Grafos por defecto - Variables y placeholders - Ejemplo de red neuronal - parte 1 - Ejemplo de red neuronal - parte 2 - Ejemplo de regresión simple con TensorFlow - Ejemplo de clasificación con TensorFlow - Ejemplo de regresión con TensorFlow - parte 1 - Ejemplo de regresión con TensorFlow - parte 2 - Ejemplo de regresión con TensorFlow - parte 3- Redes Neuronales Convolucionales - Introducción a las redes neuronales convolucionales - MNIST - Base de datos de imágenes de dígitos escritos a mano - Ejemplo con MNIST - Importar base de datos y mostrar una imagen- Redes Neuronales Recurrentes - Introducción a las redes neuronales recurrentes - Ejemplo de una red neuronal recurrente con TensorFlow - Ejemplo de series temporales - parte 1 - Ejemplo de series temporales - parte 2 - Ejemplo de series temporales - parte 3- Bibiliotecas - Estimator API - Ke
Machine Learning, BigQuery, TensorBoard, Google Cloud, TensorFlow, Deep Learning have become key industry drivers in the global job and opportunity market. This course with mix of lectures from industry experts and Ivy League academics will help engineers, MBA students and young managers learn the fundamentals of big data and data science and their applications in business scenarios. In this course you will learn1. Data Science2. Machine Learning3. BigQuery4. TensorBoard5. Google Cloud Machine Learning6. AI, Machine Learning, Deep Learning Fundamentals7. Analyzing Data8. Supervised and Unsupervised Learning9. Building a Machine Learning Model Using BigQuery 10. Building a Machine Learning Model Using GCP and Tensorboard11. Building your own model for predicting diabetes using Decision Tree
Computer Vision Web Development course will take you from the very basics right up till you are comfortable enough in creating your own web apps. By the end of the course, you will have the skills and knowledge to develop your own computer vision applications on the web. Whether it’s Custom Object Detection or simple Color Detection you can do almost everything on the web.This comprehensive course covers a range of topics, including:Basics of Web DevelopmentBasics of Computer VisionBasics of OpenCV jsComputer Vision and Web IntegrationGraphical InterfaceVideo Processing in the Browser using OpenCV.jsObject DetectionCustom Object DetectionTensorFlow for JavaScriptDeep Learning on the WebComputer Vision AdvancedCreating 10+ CV Web AppsBuilding a Photoshop Web Application with OpenCV.jsReal-Time Face Detection in the Browser with OpenCV.js & Haar Cascade ClassifierReal-time Object Detection in the Browser using YOLOv8 and TensorFlow.jsObject Detection in Images & Videos in the Browser using YOLOv8 & TensorFlow.jsPersonal Protective Equipment (PPE) Detection in the Browser using YOLOv8 and TensorFlow.jsAmerican Sign Language (ASL) Letters Detection in the Browser using YOLOv8 and TensorFlow.jsLicence Plate Detection and Recognition in the Browser using YOLOv8 and Tesseract.js
This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2020. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.
If you're a budding data enthusiast, developer, or even an experienced professional wanting to make the leap into the ever-growing world of machine learning, have you often wondered how to integrate the power of TensorFlow with the vast scalability of Google Cloud? Do you dream of deploying robust ML models seamlessly without the fuss of infrastructure management?Delve deep into the realms of machine learning with our structured guide on "Machine Learning with TensorFlow on Google Cloud." This course isn't just about theory; it's a hands-on journey, uniquely tailored to help you utilize TensorFlow's prowess on the expansive infrastructure that Google Cloud offers.In this course, you will:Develop foundational models such as Linear and Logistic Regression using TensorFlow.Master advanced architectures like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) for intricate tasks.Harness the power and convenience of Google Cloud's Colab to run Python code effortlessly.Construct sophisticated Jupyter notebooks with real-world datasets on Google Colab and Vertex.But why dive into TensorFlow on Google Cloud? As machine learning solutions become increasingly critical in decision-making, predicting trends, and understanding vast datasets, TensorFlow's integration with Google Cloud is the key to rapid prototyping, scalable computations, and cost-effective solutions.Throughout your learning journey, you'll immerse yourself in a series of projects and exercises, from constructing your very first ML model to deploying intricate deep learning networks on the cloud.This course stands apart because it bridges the gap between theory and practical deployment, ensuring that once you've completed it, you're not just knowledgeable but are genuinely ready to apply these skills in real-world scenarios.Take the next step in your machi
Course Workflow:This course is focused on Embedded Deep learning in Python . Raspberry PI 4 is utilized as a main hardware and we will be building practical projects with custom data .We will start with trigonometric functions approximation . In which we will generate random data and produce a model for Sin function approximationNext is a calculator that takes images as input and builds up an equation and produces a result .This Computer vision based project is going to be using convolution network architecture for Categorical classificationAnother amazing project is focused on convolution network but the data is custom voice recordings . We will involve a little bit of electronics to show the output by controlling our multiple LEDs using own voice .Unique learning point in this course is Post Quantization applied on Tensor flow models trained on Google Colab . Reducing size of models to 3 times and increasing inferencing speed up to 0.03 sec per input . Sections :Non-Linear Function ApproximationVisual CalculatorCustom Voice Controlled LedOutcomes After this Course : You can create Deep Learning Projects on Embedded HardwareConvert your models into Tensorflow Lite modelsSpeed up Inferencing on embedded devicesPost QuantizationCustom Data for Ai ProjectsHardware Optimized Neural NetworksComputer Vision projects with OPENCVDeep Neural Networks with fast inferencing SpeedHardware RequirementsRaspberry PI 412V Power Bank2 LEDs ( Red and Green )Jumper Wires Bread
En este curso veremos cómo implementar nuestros modelos de inteligencia artificial en una aplicación Android utilizando Tensorflow Lite. El tensorFlow lite es un conjunto de herramientas que nos ayuda a ejecutar modelos de TensorFlow en dispositivos móviles, integrados y de IoT. Esta nos permitirá realizar la inferencia en un dispositivo móvil. Implementaremos desde cero un modelo de “Regresión Lineal” en Python y lo llevaremos a Android utilizando Tensorflow Lite. Implementaremos desde cero un modelo de “Regresión en Múltiple” con normalización de datos y lo llevaremos a Android utilizando Tensorflow Lite. Implementaremos desde cero una “Red Neuronal Convolucional” para clasificar imágenes y llevaremos el modelo a Android utilizando Tensorflow Lite. Implementaremos un ejemplo de detección de objetos basado en la “Red Neuronal Convolucional” MobileNet. Implementaremos desde cero una “Red Neuronal Artificial” para clasificar dígitos utilizando el dataset MNIST y llevaremos el modelo a Android para reconocer dígitos del 0 al 9 utilizando Tensorflow Lite. Entrenamiento del algoritmo Yolo en Google Colab y despliegue en Aplicación Android. Veremos también como descargar cientos de imágenes para elaborar datasets de manera automática. Implementaremos la técnica de “Data Augmentation” para incrementar la precisión de nuestros modelos de clasificación de imágenes. Además implementaremos OpenCV para segmentar y reconocer digitos escritos a mano.Los invito cordialmente a tomar el curso en donde aprenderán a implementar sus modelos de inteligencia artificial en una aplicación Android.
This course covers RNNs, LSTMs, and GRUs in Tensorflow. It includes projects on time series prediction, music generation, language translation, image captioning, spam detection, and action recognition.
Unlock the creative potential of Generative Adversarial Networks (GANs) and Neural Style Transfer in this hands-on course, designed to guide you through the most advanced techniques in AI-driven image generation and art creation. Using TensorFlow, we will dive into the core concepts of GANs and explore their various architectures, providing you with practical skills to implement them from scratch.In the first half of the course, you'll master GANs by implementing several popular architectures:Vanilla GAN: Understand the basics of GANs and how the generator and discriminator interact.DCGAN (Deep Convolutional GAN): Learn how to generate high-quality images using convolutional layers.Wasserstein GAN (WGAN): Discover how WGAN improves stability and reduces mode collapse in GAN training.Conditional GAN (CGAN): Create conditional models that allow for more control over generated images.Pix2Pix GAN: Learn how to convert images from one domain to another, such as turning sketches into photos.Cycle GAN: Master the art of unpaired image-to-image translation, perfect for tasks like photo enhancement or style transfer.In the second part of the course, we delve into the fascinating world of Neural Style Transfer:Vanilla Neural Style Transfer: Learn how to blend the content of one image with the style of another.Feed Forward Style Transfer: Understand the advantages of using fast neural networks for style transfer.Arbitrary Style Transfer: Generate any artistic style on any content image, enabling limitless creativity.GauGAN: Create realistic images using a simple sketch, by applying a powerful
Tauche ein in die kreative Seite der Künstlichen Intelligenz – mit Generativen Neuronalen Netzwerken (GANs), Autoencodern und Adversarial Attacks. In diesem praxisorientierten Kurs lernst du, wie du mit Python, TensorFlow 2.14 und Keras eigene Deep-Learning-Modelle entwickelst, trainierst und sogar „hackst“.Nach einer kurzen Einführung in die Grundlagen von Machine Learning und Deep Learning, baust du Schritt für Schritt eigene neuronale Netze auf – von klassischen Deep Neural Networks bis hin zu verschiedenen Arten von GANs. Du verstehst nicht nur, wie diese Modelle funktionieren, sondern setzt sie auch selbst um – mit zahlreichen spannenden Coding-Sessions.Neben der Generierung realistischer Daten mit Variational Autoencodern (VAE) und der Datenkomprimierung mit klassischen Autoencodern, wirst du auch lernen, wie neuronale Netze durch gezielte Adversarial Attacks ausgetrickst werden können – und wie man sich dagegen schützt.Dieser Kurs richtet sich an alle, die ein solides Verständnis im Deep Learning aufbauen und moderne generative Modelle praktisch umsetzen möchten. Egal ob Data Science Student, KI-Enthusiast oder Entwickler – hier wirst du gefordert und gefördert.Das wirst du lernen:Grundlagen von Machine Learning & Deep LearningEigene Deep Neural Networks mit TensorFlow & Keras entwickelnAdversarial Generative Networks (GANs) verstehen und implementierenAdversarial Attacks: Netzwerke gezielt angreifen & absichernDaten komprimieren mit Autoencodern (AE)Realistische Daten generieren mit Variational Autoencodern (VAE)Arbeiten in Python (über Anaconda oder andere Installationen)Werde jetzt Teil der KI-Zukunft – mit deinem eigenen generativen Netzwerk.Let’s code the future – wir sehen uns im Kurs!Hinweis:Python wird im Kurs mit Anac
Machine learning brings together computer science and statistics to build smart, efficient models. Using powerful techniques offered by machine learning, you’ll tackle data-driven problems. The effective blend of Machine Learning with Python, scikit-learn, and TensorFlow, helps in implementing solutions to real-world problems as well as automating analytical model. This comprehensive 3-in-1 course is your one-stop solution in mastering machine learning algorithms and their implementation. Learn the fundamentals of machine learning and build your own intelligent applications. Explore popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python, scikit-learn and TensorFlow. The first course, Step-by-Step Machine Learning with Python, covers easy-to-follow examples that get you up and running with machine learning. In this course, you’ll learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. You’ll build your own models from scratch. The second course, Machine Learning with Scikit-learn, covers effective learning algorithms to real-world problems using scikit-learn. You’ll build systems that classify documents, recognize images, detect ads, and more. You’ll learn to use scikit-learn’s API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model’s performance. The third cou
Dans ce cours, vous allez découvrir et approfondir les différents aspects liés à l'apprentissage automatique avec Python. Nous utiliserons les librairies telles que Tensorflow, Keras, Pandas, Numpy, Scikit learn, ...Les travaux sont accessibles et exploitables en ligne grâce à l'utilisation des carnets Jupyter avec Google Colab. Aucune installation de logiciel spécifique sur son ordinateur n'est requise car tout le travail se fait en ligne.A chaque étape d'apprentissage de ce cours, de nouveaux modèles sont introduits. Des explications claires permettent de bien les comprendre à travers 6 thèmes d'étude :Structure de base d'un réseau de neuronesReconnaissance d'image avec un réseau de neurones à convolution 2DTraitement d'image avec un réseau de neurones profond à convolution 2DSystèmes de recommandations et d'analyse des ressentisDétection d'anomalies dans les donnéesAnalyse et prédiction sur les séries temporellesLes activités en Python expliquent clairement comment les exploiter. Des exercices sont régulièrement proposés pour consolider votre apprentissage.D'une durée totale de 19,5 heures, ce cours vous permettra d'être à l'aise avec les outils actuels du Deep Learning. Vous serez alors capable d'utiliser ces ressources pour créer vos propres projets et d'approfondir avec sérénité et en autonomie vos connaissances dans ce domaine.=== Prérequis ===Vous n'avez pas besoin d'être un spécialiste du langage Python. En effet, au fur et à mesure de votre progression, vous manierez ce langage et découvrirez les subtilités liées à son utilisation.Si vous êtes complètement débutant en Deep Learning, alors ce cours est fait pour vous. Ce cours est structuré de manière progressive pour acquérir petit à petit les bases du de
Part of the DeepLearning.AI TensorFlow Developer Specialization, this course teaches best practices for using TensorFlow to build scalable AI-powered algorithms. You'll learn advanced techniques to improve computer vision models, including strategies to prevent overfitting like augmentation and dropout.
This course teaches how to apply knowledge of classification models and embeddings to build a machine learning pipeline that functions as a recommendation engine using TensorFlow on Google Cloud Platform.
A-Z™ | Tensorflow ile Derin ÖğrenmeKursumuzda klasik ve derin öğrenme tabanlı yöntemlerini kullanarak sınıflandırma nasıl yapıldığını öğrenip, Tensorflow kütüphaneleriyle gerçek hayat projeleri yapacağız.Projelerle Yapay Zeka ve Bilgisayarlı Görü Kursu İçeriğiGiriş BölümüDerin Öğrenme TeoriDerin Öğrenme NedirYapay Sinir AğlarıAktivasyon FonksiyonlarıOptimizasyon AlgoritmalarıLoss (Kayıp) FonksiyonlarıDerin Öğrenme TeoriCNN (Convolutional Neural Networks) TeoriEvrişim İşlemiCNN (Convolutional Neural Networks)Piksel Ekleme (Padding)Adım Kaydırma (Stride)Ortaklama (Pooling)Ek Teori Epoch ve Batch SizeDropoutEarly StoppingLearning RateTensorflow ile Derin ÖğrenmeTensorflow TemelleriVeriyi HazırlamaModel Oluşumu SequentialModel EgitimiModel Testi | 1. KısımModel Testi | 2. KısımModeli Kaydetme/Yükleme - Save/LoadModel Sonuçlarını GörselleştirmeModelin Ara Katmalarını GörselleştirmeFunctional Bir Model OluşturmaCallbacks | 1. kısımCallbacks | 2. kısımData Augmentation - Veri Arttırma | 1. KısımData Augmentation - Veri Arttırma | 2. KısımTransfer Learning - VGGHazır Model Kullanma - VGGTensorflow ile Trafik İşaretlerini SınıflandırmaVeriyi HazırlamaModel Eğitimi ve TestReal Time'da TestTensorflow'da Weights & Biases (WandB) | Özel VeriWandb ile Keras'da Temel FonsiyonlarWandb ile Keras'da SweeplerWandb ile Keras'da Sweep - Bonus VideoTensorflow Lite - Android App - Object detection - İmage ClassificationEfficientDet Lite Model Eğitimi - Object detectionEfficientDet Lite Modeli Android'de Çalıştırma 1 - Object detectionEfficientDet Lite Modeli Androi
A área de Deep Learning (Aprendizagem Profunda) está relacionada a aplicação das redes neurais artificiais na resolução de problemas complexos e que requerem artifícios computacionais avançados. Existem diversas aplicações práticas que já foram construídas utilizando essas técnicas, tais como: carros autônomos, descoberta de novos medicamentos, cura e diagnóstico antecipado de doenças, geração automática de notícias, reconhecimento facial, recomendação de produtos, previsão dos valores de ações na bolsa de valores e até mesmo a geração automática de roteiros de filmes! Nesses exemplos, a técnica base utilizada são as redes neurais artificiais, que procuram "imitar" como o cérebro humano funciona e são consideradas hoje em dia como as mais avançadas no cenário de Machine Learning (Aprendizagem de Máquina). E a maioria dessas aplicações foram desenvolvidas utilizando a biblioteca TensorFlow do Google, que hoje em dia é a ferramenta mais popular e utilizada nesse cenário. Por isso, é de suma importância que profissionais ligados à área de Inteligência Artificial e Machine Learning saibam como trabalhar com essa biblioteca, já que várias grandes empresas a utilizam em seus sistemas, tais como: Airbnd, Airbus, eBay, Dropbox, Intel, IBM, Uber, Twitter, Snapchat e também o próprio Google!A área de Deep Learning é atualmente um dos campos de trabalho mais relevantes da Inteligência Artificial, sendo que o mercado de trabalho dessa área nos Estados Unidos e em vários países da Europa está em grande ascensão; e a previsão é que no Brasil cada vez mais esse tipo de profissional seja requisitado! Inclusive alguns estudos apontam que o conhecimento dessa área será em breve um pré-requisito para os profissionais de Tecnologia da Informação!E para levar você até essa área, neste curso você terá uma visão teórica e principalmente prática sobre as principais e mais modernas técnicas de Deep Learning utilizando o Tensor
Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common?They are all masters of deep learning. We often hear about AI, or self-driving cars, or the ‘algorithmic magic’ at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.Cool, that sounds like a really important skill; how do I become a Master of Deep Learning?There are two routes you can take: The unguided route – This route will get you where you want to go, eventually, but expect to get lost a few times. If you are looking at this course you’ve maybe been there. The 365 route – Consider our route as the guided tour. We will take you to all the places you need, using the paths only the most experienced tour guides know about. We have extra knowledge you won’t get from reading those information boards and we give you this knowledge in fun and easy-to-digest methods to make sure it really sticks.Clearly, you can talk the talk, but can you walk the walk? – What exactly will I get out of this course that I can’t get anywhere else?Good question! We know how interesting Deep Learning is and we love it! However, we know that the goal here is career progression, that’s why our course is business focused and gives you real world practice on how to use Deep Learning to optimize business performance.We don’t just scratch the surface either – It’s not called ‘Skin-Deep’ Learning after all. We fully explain the theory from the mathematics behind the algorithms to the state-of-the-art initialization methods, plus so much more.Theory is no good without putting it into practice, is it? That’s why we give you plenty of opportunities to put this theory to use. Implement cutting edge
TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. So, if you’re a Python developer who is interested in learning how to create applications and perform image processing using TensorFlow, then you should surely go for this Learning Path. Packt’s Video Learning Path is a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. The highlights of this Learning Path are: Learn how to create image processing applications using free tools and librariesPerform advanced image processing with TensorFlowAPIsUnderstand and optimize various features of TensorFlow by building deep learning state-of-the-art models Let's take a quick look at your learning journey. This Learning Path starts off with an introduction to image processing. You will then walk through graph tensor which is used for image classification. Starting with the basic 2D images, you will gradually be taken through more complex images, colors, shapes, and so on. You will also learn to make use of Python API to classify and train your model to identify objects in an image. Next, you will learn about convolutional neural networks (CNNs), its architecture, and why they perform well in the image take. You will then dive into the different layers available in TensorFlow. You will also learn to construct the neural network feature extractor to embed images into a dense and rich vector space. Moving ahead, you will learn to construct efficient CNN architectures with CNN Squeeze layers and delayed downsampling. You will learn about residual learning with skip connections and deep residual blocks, and see how to implement a deep residual neural network for image recognition. Next, you will find out about Google’s Incep
Dieser Kurs ist dein umfassender Einstieg in die Welt des Deep Learnings – mit einem klaren Fokus auf Praxis, fundierter Theorie und moderner Python-Entwicklung mit TensorFlow 2 und Keras.Statt nur Code-Schnipsel zu kopieren, lernst du wirklich zu verstehen, wie neuronale Netze funktionieren – von der mathematischen Basis bis zur Anwendung. Du wirst eigene Modelle Schritt für Schritt selbst aufbauen und trainieren, Bilddaten analysieren und sogar Texte mit KI verarbeiten.Du startest mit den Grundlagen des Machine Learning und neuronaler Netzwerke – und steigst dann tief in die wichtigsten Netzarchitekturen ein: Von klassischen Fully Connected Networks über CNNs für Bildverarbeitung bis zu RNNs/LSTMs für Zeitreihen und Texte. Dabei kommen State-of-the-art Modelle wie ResNet und DenseNet ebenfalls nicht zu kurz.Auch Natural Language Processing (NLP) ist Teil des Kurses – perfekt, um moderne KI-Anwendungen wie Chatbots oder Textklassifizierer zu entwickeln.Kursinhalte im Überblick:Einführung in Machine Learning und neuronale NetzeMathematische Grundlagen (z. B. Aktivierungsfunktionen, Backpropagation)Eigene Modelle in TensorFlow 2 und Keras entwickelnVisualisierung und Debugging mit TensorBoardDigitale Bildverarbeitung mit CNNsModerne Architekturen: ResNet, DenseNetSequenzmodelle: RNNs und LSTM für zeitabhängige DatenEinstieg in Natural Language Processing (NLP) mit KerasPraxisnahe Projekte und ÜbungenZiel:Werde fit im Umgang mit modernen KI-Technologien und baue deine eigenen Deep-Learning-Modelle – fundiert, praxisnah, professionell.<p
Explore generative AI with Python and TensorFlow 2, mastering advanced algorithms, implementing models, and leveraging cloud resources to future-proof your skills and lead the GenAI revolution.
A specialization that teaches how to deploy machine learning models on devices, train and run models in browsers and mobile applications, and retrain deployed models while protecting privacy.
Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,... With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and HuggingfaceYou will learn:The Basics of Tensorflow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)Binary Classification with Malaria detection Multi-class Classification with Human Emotions DetectionTransfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If you are
Learn Deep Learning A-Z: Hands-On Neural Networks
Immerse yourself in the cutting-edge world of deep learning with TensorFlow through this comprehensive masterclass. Starting with an insightful overview and the scenario of perceptron, progress to creating neural networks, performing multiclass classification, and gaining a deep understanding of convolutional neural networks (CNN). Explore image processing, convolution intuition, and classifying photos of dogs and cats using TensorFlow. Understand the layers of deep learning neural networks and harness the power of transfer learning for advanced concepts. Engage in real-world projects like Face Mask Detection and Linear Model Implementation. Elevate your skills to master TensorFlow, enabling you to build and deploy powerful deep learning models.This masterclass is designed for individuals passionate about deep learning, whether beginners or experienced practitioners. Uncover the secrets of TensorFlow and take your understanding of deep learning to new heights!Section 1: Machine Learning ZERO to HERO - Hands-on with TensorFlowThis foundational section serves as a comprehensive introduction to machine learning using TensorFlow. It begins with essential concepts, including understanding the fundamentals of machine learning and how machines learn. The section then progresses to practical aspects, guiding learners through setting up their workstations, exploring different programming languages, and understanding the functions of Jupyter notebooks. The focus expands to include third-party libraries, with an emphasis on NumPy and Pandas for efficient data manipulation and analysis. The section concludes by introducing data visualization using Matplotlib and Seaborn, providing a solid groundwork for the subsequent sections.Section 2: Project On TensorFlow - Face Mask Detection ApplicationIn this hands-on project section, learners apply their knowledge to a real-world application by building a Face Mask Detection application using TensorFlo
For those with some machine learning experience, this course provides a deeper dive into deep learning with TensorFlow. It covers advanced topics like building custom neural networks, and working with text and sequence data.
You want to start developing deep learning solutions, but you do not want to lose time in mathematics and theory?You want to conduct deep learning projects, but do not like the hassle of tedious programming tasks?Do you want an automated process for developing deep learning solutions?This course is then designed for you! Welcome to Deep Learning in Practice, with NO PAIN!This course is the first course on a series of Deep Learning in Practice Courses of Anis Koubaa, namelyDeep Learning in Practice I: Tensorflow 2 Basics and Dataset Design (this course): the student will learn the basics of conducting a classification project using deep neural networks, then he learns about how to design a dataset for industrial-level professional deep learning projects. Deep Learning in Practice II: Transfer Learning and Models Evaluation: the student will learn how to manage complex deep learning projects and develop models using transfer learning using several state-of-the-art CNN algorithms. He will learn how to develop reusable projects and how to compare the results of different deep learning models in an automated manner. Deep Learning in Practice III: Face Recognition. The student will learn how to build a face recognition app in Tensorflow and Keras.Deep Learning in Practice I: Basics and Dataset DesignThere are plenty of courses and tutorials on deep learning. However, some practical skills are challenging to find in this massive bunch of deep learning resources, and that someone would spend a lot of time to get these practical skills.This course fills this gap and provides a series of practical lectures with hands-on projects through which I introduce the best practices that deep learning practitioners have to know to conduct deep learning projects.I have
Artificial Intelligence (AI) revolution is here and TensorFlow 2.0 is finally here to make it happen much faster! TensorFlow 2.0 is Google’s most powerful, recently released open source platform to build and deploy AI models in practice.AI technology is experiencing exponential growth and is being widely adopted in the Healthcare, defense, banking, gaming, transportation and robotics industries. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab.The course provides students with practical hands-on experience in training Artificial Neural Networks and Convolutional Neural Networks using real-world dataset using TensorFlow 2.0 and Google Colab. This course covers several technique in a practical manner, the projects include but not limited to:(1) Train Feed Forward Artificial Neural Networks to perform regression tasks such as sales/revenue predictions and house price predictions(2) Develop Artificial Neural Networks in the medical field to perform classification tasks such as diabetes detection.(3) Train Deep Learning models to perform image classification tasks such as face detection, Fashion classification and traffic sign classification.(4) Develop AI models to perform sentiment analysis and analyze customer reviews.(5) Perform AI models visualization and assess their performance using Tensorboard(6) Deploy AI models in practice using Tensorflow 2.0 ServingThe course is targeted towards students wanting to gain a fundamental understanding of how to build and deploy models in Tensorflow 2.0. Basic knowledge of programming is recommended. However, these topics will be extensively covered during early course lectures; therefore, the course has no prerequisites, and is open to any student with basic programming knowledge. Students who enroll in this course will master AI and Deep Learning techn
A warm welcome to the Deep Learning with TensorFlow course by Uplatz.TensorFlow is an end-to-end open-source machine learning / deep learning platform. It has a comprehensive ecosystem of libraries, tools, and community resources that lets AI/ML engineers, scientists, analysts build and deploy ML-powered deep learning applications. The name TensorFlow is derived from the operations which neural networks perform on multidimensional data arrays or tensors. Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain.TensorFlow is a machine learning framework that Google created and used to design, build, and train deep learning models. You can use the TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.In simple words, TensorFlow is an open-source and most popular deep learning library for research and production. TensorFlow in Python is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks. TensorFlow manages to combine a comprehensive and flexible set of technical features with great ease of use.There have been some remarkable developments lately in the world of artificial intelligence, from much publicized progress with self-driving cars to machines now composing imitations or being really good at video games. Central to these advances are a number of tools around to help derive deep learning and other machine learning models, with Torch, Caffe, and Theano amongst those at the fore. However, since Google Brain went open source in November 20
TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming. What's covered: Deep learning basics: What a neuron is; how neural networks connect neurons to 'learn' complex functions; how TF makes it easy to build neural network modelsUsing Deep Learning for the famous ML problems: regression, classification, clustering and autoencodingCNNs - Convolutional Neural Networks: Kernel functions, feature maps, CNNs v DNNs RNNs - Recurrent Neural Networks: LSTMs, Back-propagation through time and dealing with vanishing/exploding gradientsUnsupervised learning techniques - Autoencoding, K-means clustering, PCA as autoencoding Working with imagesWorking with documents and word embeddingsGoogle Cloud ML Engine: Distributed training and prediction of TF models on the cloudWorking with TensorFlow estimators
Interested in using Machine Learning in JavaScript applications and websites? Then this course is for you!This is the tutorial you've been looking for to become a modern JavaScript machine learning master in 2024. It doesn’t just cover the basics, by the end of the course you will have advanced machine learning knowledge you can use on you resume. From absolute zero knowledge to master - join the TensorFlow.js revolution.This course has been designed by a specialist team of software developers who are passionate about using JavaScript with Machine Learning. We will guide you through complex topics in a practical way, and reinforce learning with in-depth labs and quizzes.Throughout the course we use house price data to ask ever more complicated questions; “can you predict the value of this house?”, “can you tell me if this house has a waterfront?”, “can you classify it as having 1, 2 or 3+ bedrooms?”. Each example builds on the one before it, to reinforce learning in easy and steady steps.Machine Learning in TensorFlow.js provides you with all the benefits of TensorFlow, but without the need for Python. This is demonstrated using web based examples, stunning visualisations and custom website components.This course is fun and engaging, with Machine Learning learning outcomes provided in bitesize topics:Part 1 - Introduction to TensorFlow.jsPart 2 - Installing and running TensorFlow.jsPart 3 - TensorFlow.js Core ConceptsPart 4 - Data Preparation with TensorFlow.jsPart 5 - Defining a modelPart 6 - Training and Testing in TensorFlow.jsPart 7 - TensorFlow.js PredictionPart 8 - Binary ClassificationPart 9 - Multi-class ClassificationPart 10 - Conclusion & Next StepsAs a bonus, for every student, we provide you wit
Interested in the field of Machine Learning? Then this course is for you! This course has been designed by experts so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative field of ML. This course is fun and exciting, but at the same time we dive deep into Machine Learning. we will be covering the following topics in a well crafted way: Tensors and TensorFlow on the Cloud - what neural networks, Machine learning and deep learning really are, how neurons work and how neural networks are trained. - Datalab, Linear regressions, placeholders, variables, image processing, MNIST, K- Nearest Neighbors, gradient descent, softmax and more Moreover, the course is packed with practical exercises which are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. Course Overview Module 1- Introduction Gcloud Introduction Labs Module 2 - Hands on GCP Labs Module 2-Datalab Module 3-Machine Learning & Tensorflow Introduction to Machine Learning, Typical usage of Mechine Learning, Types, The Mechine Learning block diagram, Deep learning & Neural Networks, Labels, Understanding Tenser Flow, Computational Graphs, Tensors, Linear regression , Placeholders & variables, Image processing in Tensor Flow, Image as tensors, M-NIST – Introduction, K-nearest neighbors Algorithm, L1 distance, Steps in K- nearest neighbour implementation, Neural Networks i
Welcome to a game-changing learning experience with "ChatGPT for Deep Learning using Python Keras and TensorFlow". This unique course combines the power of ChatGPT with the technical depth of Python, Keras, and TensorFlow to offer you an innovative approach to tackling complex Deep Learning projects. Whether you're a beginner or a seasoned Data Scientist, this course will significantly enhance your skill set, making you more proficient and efficient in your work.Why This Course?Deep learning and Artificial Intelligence are revolutionizing industries across the globe, but mastering these technologies often requires a significant time investment (for theory and coding). This course cuts through the complexity, leveraging ChatGPT to simplify the learning curve and expedite your project execution. You'll learn how to harness the capabilities of AI to streamline tasks from data processing to complex model training, all without needing exhaustive prior knowledge of the underlying mathematics and Python code.Comprehensive Learning ObjectivesBy the end of this course, you will be able to apply the most promising ChatGPT prompting strategies and techniques in real-world scenarios:ChatGPT Integration: Utilize ChatGPT effectively to automate and enhance various stages of your Data Science projects, including coding, model development, and result analysis.Data Management: Master techniques for loading, cleaning, and visualizing data using Python libraries like Pandas, Matplotlib, and Seaborn.Deep Learning Modeling: Gain hands-on experience in constructing and fine-tuning Neural Networks for tasks such as Image Recognition with CNNs, Time Series prediction with RNNs and LSTMs, and classification and regression with Feedforward Neural Networks (FNN), using ChatGPT as your assistant.<strong
As a practitioner of Deep Learning, I am trying to bring many relevant topics under one umbrella in the following topics. Deep Learning has been most talked about for the last few years and the knowledge has been spread across multiple places.1. The content (80% hands-on and 20% theory) will prepare you to work independently on Deep Learning projects2. Foundation of Deep Learning TensorFlow 2.x3. Use TensorFlow 2.x for Regression (2 models)4. Use TensorFlow 2.x for Classifications (2 models)5. Use Convolutional Neural Net (CNN) for Image Classifications (5 models)6. CNN with Image Data Generator7. Use Recurrent Neural Networks (RNN) for Sequence data (3 models)8. Transfer learning9. Generative Adversarial Networks (GANs)10. Hyperparameters Tuning11. How to avoid Overfitting12. Best practices for Deep Learning and Award-winning Architectures
Have you been looking for a course that teaches you effective machine learning in scikit-learn and TensorFlow 2.0? Or have you always wanted an efficient and skilled working knowledge of how to solve problems that can't be explicitly programmed through the latest machine learning techniques?If you're familiar with pandas and NumPy, this course will give you up-to-date and detailed knowledge of all practical machine learning methods, which you can use to tackle most tasks that cannot easily be explicitly programmed; you'll also be able to use algorithms that learn and make predictions or decisions based on data.The theory will be underpinned with plenty of practical examples, and code example walk-throughs in Jupyter notebooks. The course aims to make you highly efficient at constructing algorithms and models that perform with the highest possible accuracy based on the success output or hypothesis you've defined for a given task.By the end of this course, you will be able to comfortably solve an array of industry-based machine learning problems by training, optimizing, and deploying models into production. Being able to do this effectively will allow you to create successful prediction and decisions for the task in hand (for example, creating an algorithm to read a labeled dataset of handwritten digits).About the AuthorSamuel Holt has several years' experience implementing, creating, and putting into production machine learning models for large blue-chip companies and small startups (as well as within his own companies) as a machine learning consultant.He has machine learning lab experience and holds an MEng in Machine Learning and Software Engineering from Oxford University, where he won four awards for academic excellence.Specifically, he has built systems that run in production using a combination of scikit-learn and TensorFlow involving automated customer support, implementing document OCR, detecting vehicles in the case of s
This course is designed to make you proficient in training and evaluating deep learning based object detection models. Specifically, you will learn about Faster R-CNN, SSD and YOLO models. For each of these models, you will first learn about how they function from a high level perspective. This will help you build the intuition about how they work. After this, you will learn how to leverage the power of Tensorflow 2 to train and evaluate these models on your local machine. Finally, you will learn how to leverage the power of cloud computing to improve your training process. For this last part, you will learn how to use Google Cloud AI Platform in order to train and evaluate your models on powerful GPUs offered by google. I designed this course to help you become proficient in training and evaluating object detection models. This is done by helping you in several different ways, including :Building the necessary intuition that will help you answer most questions about object detection using deep learning, which is a very common topic in interviews for positions in the fields of computer vision and deep learning.By teaching you how to create your own models using your own custom dataset. This will enable you to create some powerful AI solutions.By teaching you how to leverage the power of Google Cloud AI Platform in order to push your model's performance by having access to powerful GPUs.
Machine learning and Deep Learning have been gaining immense traction lately, but until now JavaScript developers have not been able to take advantage of it due to the steep learning curve involved in learning a new language. Here comes a browser-based JavaScript library, TensorFlow.js to your rescue using which you can train and deploy machine learning models entirely in the browser. If you’re a JavaScript developer who wants to enter the field ML and DL using TensorFlow.js, then this course is for you.Towards the end of this course, you will be able to implement Machine Learning and Deep Learning for your own projects using JavaScript and the TensorFlow.js library.This course is project-based so you will not be learning a bunch of useless coding practices. At the end of this course, you will have real-world apps to use in your portfolio. We feel that project-based training content is the best way to get from A to B. Taking this course means that you learn practical, employable skills immediately.You can use the projects you build in this course to add to your LinkedIn profile. Give your portfolio fuel to take your career to the next level.Learning how to code is a great way to jump into a new career or enhance your current career. Coding is the new math and learning how to code will propel you forward in any situation. Learn it today and get a head start for tomorrow. People who can master technology will rule the future.
Ce cours vous guidera dans l'utilisation du dernier Framework TensorFlow 2 de Google pour créer des Réseaux de Neurones Artificiels pour le Deep Learning ! Ce cours a pour but de vous donner un guide facile à comprendre sur les complexités du Framework TensorFlow version 2.x de Google (dernière version à jour).Nous nous attacherons à comprendre les dernières mises à jour de TensorFlow et à exploiter l'API de Keras (l'API officielle de TensorFlow 2) pour construire rapidement et facilement des modèles. Dans ce cours, nous construirons des modèles pour prédire des prix futurs de maisons, classer des images médicales, prédire les données de ventes futures, générer artificiellement un nouveau texte complet et bien plus encore... !Ce cours est conçu pour équilibrer la théorie et la mise en œuvre pratique, avec des guides de code complets de type "Notebook Google Colab" et des slides et notes faciles à consulter. Il y a également de nombreux exercices pour tester vos nouvelles compétences au cours de la formation !Ce cours couvre une grande variété de sujets, notamment :Cours accéléré sur la bibliothèque NumPyCours intensif et accéléré sur l'analyse des données avec la bibliothèque PandasCours accéléré sur la visualisation de donnéesPrincipes de base des réseaux de neuronesPrincipes de base de TensorFlowNotions de syntaxe de KerasRéseaux de Neurones Artificiels (ANNs)Réseaux à forte densité de connexionRéseaux de Neurones Convolutifs (CNNs)Réseaux de Neurones Récurrents (RNNs)AutoEncodersRéseaux Adversatifs Générateurs (GANs)Déploiement de TensorFlow en production avec Flasket bien plus encore !Keras, une API standard conviviale pour le Deep Learning, elle sera l'API centrale de haut niveau u
For those with an intermediate to advanced understanding of computer vision, this course covers advanced topics like deep learning, convolutional neural networks (CNNs), object detection, image segmentation, and generative models. It is taught by a renowned expert in the field and is designed for students with a strong programming background.
A step-by-step guide on how to integrate TensorFlow Lite with Flutter to build AI-powered mobile apps for both iOS and Android from a single codebase. It covers using pre-trained models and training custom models.
Learn TensorFlow Developer Professional Certificate
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