Advanced Computer Vision

Computer Vision Masterclass with OpenCV and Deep Learning

PyTorch for Deep Learning and Computer Vision

Learn Computer Vision A-Z: Learn OpenCV, GANs and Deep Learning

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 flowers Detection of over 80 different objects Creating new images using style transfer Use of GANs (generative adversarial network) to complete missing parts of images Recognition of actions in videos Text polarity classification (positive and negative)Use o

Welcome to "Machine Learning: Modern Computer Vision & Generative AI," a cutting-edge course that explores the exciting realms of computer vision and generative artificial intelligence using the KerasCV library in Python. This course is designed for aspiring machine learning practitioners who wish to explore the fusion of image analysis and generative modeling in a streamlined and efficient manner.Course Highlights:KerasCV Library: We start by harnessing the power of the KerasCV library, which seamlessly integrates with popular deep learning backends like TensorFlow, PyTorch, and JAX. KerasCV simplifies the process of writing deep learning code, making it accessible and user-friendly.Image Classification: Gain proficiency in image classification techniques. Learn how to leverage pre-trained models with just one line of code, and discover the art of fine-tuning these models to suit your specific datasets and applications.Object Detection: Dive into the fascinating world of object detection. Master the art of using pre-trained models for object detection tasks with minimal effort. Moreover, explore the process of fine-tuning these models and learn how to create custom object detection datasets using the Label Img GUI program.Generative AI with Stable Diffusion: Unleash the creative potential of generative artificial intelligence with Stable Diffusion, a powerful text-to-image model developed by Stability AI. Explore its capabilities in generating images from textual prompts and understand the advantages of KerasCV's implementation, such as XLA compilation and mixed precision support, which push the boundaries of generation speed and quality.Course Objectives:Develop a strong foundation in modern computer vision techniques, including image classification and object detection.Acquire hands-on experience in using pre-t

Welcome to the future of AI, where mastering the art of prompt engineering can unlock a world of possibilities. As AI tools like ChatGPT, Gemini, and Bing AI continue to reshape industries, from marketing and content creation to customer service and education, the ability to craft effective prompts is quickly becoming one of the most valuable skills in the digital age.This course, "Master Prompt Engineering for Generative AI," is your gateway to understanding how to harness the full potential of AI. Whether you’re looking to streamline your workflow, enhance creativity, or improve communication, learning how to guide AI effectively opens up limitless opportunities.In today’s rapidly evolving job market, companies are searching for people who can do more than just use AI, they want individuals who can control and optimize it. By mastering prompt engineering, you’ll not only stay ahead of the curve but position yourself as a key asset in any field that’s embracing AI-driven innovation.From generating personalized content to optimizing business processes, the possibilities with prompt engineering are endless. This skill gives you the power to leverage AI for tailored solutions, making you indispensable in any team or project. Whether you’re an entrepreneur, content creator, developer, or educator, understanding prompt engineering will give you a competitive edge and the freedom to innovate in ways that were previously unimaginable.Don’t miss the opportunity to shape your future with AI. Start mastering prompt engineering today, and discover how a few well-crafted words can change everything.Disclaimer:Please note that some theoretical chapters of this course feature AI-generated voice/video narration. While I strive to provide a seamless and engaging learning experience, the use of AI-generated voice/video helps ensure consistency and clarity in the delivery of content. I appre

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.This is one of the most exciting courses I’ve done and it really shows how fast and how far deep learning has come over the years.When I first started my deep learning series, I didn’t ever consider that I’d make two courses on convolutional neural networks.I think what you’ll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover.Let me give you a quick rundown of what this course is all about:We’re going to bridge the gap between the basic CNNs architecture you already know and love, to modern, novel architectures such as VGG, Res Net, and Inception (named after the movie which by the way, is also great!)We’re going to apply these to images of blood cells, and create a system that is a better medical expert than either you or I. This brings up a fascinating idea: that the doctors of the future are not humans, but robots.In this course, you’ll see how we can turn a CNNs into an object detection system, that not only classifies images but can locate each object in an image and predict its label.You can imagine that such a task is a basic prerequisite for self-driving vehicles. (It must be able to detect cars, pedestrians, bicycles, traffic lights, etc. in real-time)We’ll be looking at a state-of-the-art algorithm called SSD which is both faster and more accurate than its predecessors.Another very popular computer vision task that makes use of CNNs is called neural style transfer.This is

Are you looking to ace your next data scientist or data analyst interview? Look no further! This comprehensive Udemy course, "Machine Learning & Data Science Interview Guide: 2025" is designed to equip you with the knowledge and skills necessary to excel in your data science job interviews.600+ Most Asked Interview Questions around Wide topics:Curated selection covering essential topics frequently tested during interviews.Dives deep into various domains, including Python, SQL, Statistics and Mathematics, Machine Learning and Deep Learning, Power BI, Advanced Excel, and Behavioral and Scenario-based questions.Python Section (100 Questions):Tests proficiency in coding with Python.Ensures a strong understanding of this popular programming language.SQL Section (100 Questions):Sharpens SQL querying skills.Tests knowledge of database querying and manipulation.Statistics and Mathematics Section (100 Questions):Solidifies understanding of foundational concepts.Covers essential statistical and mathematical principles.Machine Learning and Deep Learning Section (135 Questions):Explores theoretical knowledge and practical application.Prepares for ML and DL-related interview questions.Power BI and Advanced Excel Sections (105 Questions):Demonstrates expertise in data visualization and analysis tools.Covers a range of topics in Power BI and Advanced Excel functionalities.To round off your interview preparation, the course includes 60 questions that focus on behavioral and scenario-based aspects,

Welcome to Modern Computer Vision TensorFlow, Keras & PyTorch! 2025AI and Deep Learning are transforming industries and one of the most intriguing parts of this AI revolution is in Computer Vision!Update for 2025: Modern Computer Vision Course We're excited to bring you the latest updates for our 2024 modern computer vision course. Dive into an enriched curriculum covering the most advanced and relevant topics in the field:YOL Ov8: Cutting-edge Object RecognitionDINO-GPT4V: Next-Gen Vision Models Meta CLIP for Enhanced Image Analysis Detectron2 for Object Detection Segment Anything Face Recognition Technologies Generative AI Networks for Creative Imaging Transformers in Computer Vision Deploying & Productionizing Vision Models Diffusion Models for Image Processing Image Generation and Its Applications Annotation Strategy for Efficient Learning Retrieval Augmented Generation (RAG)Zero-Shot Classifiers for Versatile Applications Using Roboflow: Streamlining Vision Workflows What is Computer Vision?But what exactly is Computer Vision and why is it so exciting? Well, what if Computers could understand what they’re seeing through cameras or in images? The applications for such technology are endless from medical imaging, military, self-driving cars, security monitoring, analysis, safety, farming, industry, and manufacturing! The list is endless. Job demand for Computer Vision workers are skyrocketing

Are you ready to unlock the power of deep learning and revolutionize your career? Dive into the captivating realm of Deep Learning with our comprehensive course Deep Learning: Convolutional Neural Networks (CNNs) using Python and PyTorch. Discover the power and versatility of CNNs, a cutting-edge technology revolutionizing the field of artificial intelligence. With hands-on Python tutorials, you'll unravel the intricacies of CNNs architectures, mastering their design, implementation, and optimization. One of the key advantages of deep CNNs is its ability to automatically learn features at different levels of abstraction. Lower layers of the network learn low-level features, such as edges or textures, while higher layers learn more complex and abstract features. This hierarchical representation allows deep learning models to capture and understand complex patterns in the data, enabling them to excel in tasks such as image recognition, natural language processing, speech recognition, and many others.Introducing our comprehensive deep CNNs with python course, where you'll dive deep into Convolutional Neural Networks and emerge with the skills you need to succeed in the modern era of AI. Computer Vision refers to AI algorithms designed to extract knowledge from images or videos. Computer vision is a field of artificial intelligence (AI) that enables computers to understand and interpret visual information from digital images or videos. It involves developing deep learning algorithms and techniques that allow machines to analyze, process, and extract meaningful insights from visual data, much like the human visual system. Convolutional Neural Networks (CNNs) are most commonly used Deep Learning technique for computer vision tasks. CNNs are well-suited for processing grid-like input data, such as images, due to their ability to capture spatial hierarchies and local patterns.In today's data-driven world, Convolutional Neural Networks  stand at the forefront of image rec

This is a hands-on, project-based course designed to help you master the foundations for unsupervised machine learning in Python.We’ll start by reviewing the Python data science workflow, discussing the techniques &amp; applications of unsupervised learning, and walking through the data prep steps required for modeling. You’ll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.From there we'll fit, tune, and interpret 3 popular clustering models using Scikit-Learn. We’ll start with K-Means Clustering, learn to interpret the output’s cluster centers, and use inertia plots to select the right number of clusters. Next, we’ll cover Hierarchical Clustering, where we’ll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we’ll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.We’ll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You’ll learn to tune and interpret the results of each model and visualize the anomalies using pair plots.Next, we’ll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We’ll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization.Last but not least, we’ll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.<

This is a crash course, but an in-depth course, which will develop you as a Machine learning specialist. Designed with solutions to real life life problems, this will be a boon for your ongoing projects and the organization you work for. Students, Professors and machine learning consultants will find the course interesting, hassle free and up-to-date. Surely, the students will be employable Machine Learning Engineers and data scientists. Given by an enthusiastic and expert professor after testing it in classrooms and projects several times. The students can carry out a number of projects using this course. This exemplary, engaging, enlightening and enjoyable course is organized as seven interesting modules, with abundant worked examples in the form of code executed on Jupyter Notebook. It is important that data is visualized before attempting to carryout machine learning and hence we start the course with a module on data visualization. This is followed by a full blown and enjoyable exposure to Regression covering simple linear regression, polynomial regression, multiple linear regression.&nbsp; Regression is followed by extensive discussions on another important supervised learning algorithms on Classification. We carry out modeling using classification strategies such as logistic regression, Naive Bayes classifier, support vector machine, K nearest neighbor, Decision trees, ensemble learning, classification and regression trees, random forest and boosting - ada boost, gradient boosting. From supervised learning we move on to discuss about unsupervised learning - clustering for unlabelled data. We study the hierarchical, k means, k medoids and Agglomerative Clustering. It is not enough to know the algorithms, but also strategies such as bias variance trade off and curse of dimensionality to be successful in this challenging field of current and futuristic importance. We also carry out Principal Component analysis and Linear discriminant analysis to de

Are you interested in leveraging the power of AI to streamline your Data Science projects?Do you want to learn how to use ChatGPT and GenAI technologies to design efficient data science workflows and create stunning data visualizations?Are you a data scientist, project manager, or entrepreneur keen on leveraging AI tools to kick-start and execute data science projects efficiently?If the answer is yes to any of these questions, this course is tailor-made for you!ChatGPT, developed by OpenAI, is an advanced language model that can be applied to various data science tasks, including data preparation, feature engineering, data analysis, and report generation. This course, "ChatGPT for Data Science and Data Analysis in Python", will help you significantly use ChatGPT to speed up your data science projects.Data Science continues to be one of the most in-demand fields, offering numerous career opportunities across sectors. With the advent of AI technologies like ChatGPT, it's now possible to execute data science projects more efficiently, reducing time and effort significantly.&nbsp;And we will teach you how. Here's what sets this course apart:A focus on practical application: From prompt engineering to text classification, you will learn to apply ChatGPT in real-world data science contexts.Step-by-step guide: Each module is designed to build on the previous one, ensuring a comprehensive understanding of how to use ChatGPT for various stages of a data science project.Collaborative learning: Learn how to use ChatGPT to improve team communication, a critical skill in any data science project.What will you learn?How to design efficient prompts in ChatGPT for optimal results.Techniques to initiate data science projects using ChatGPT, potentially reducing start-up time by

A warm welcome to the Generative AI with LL Ms, Prompting, Automation &amp; Agents course by Uplatz.Generative AI (Generative Artificial Intelligence) refers to a type of artificial intelligence that is capable of creating new content—such as text, images, audio, code, and more—rather than simply analyzing existing data. It mimics human creativity by learning from large datasets and generating outputs that resemble original, human-made content.What It Does Traditional AI systems are good at recognizing patterns or making predictions based on existing data. Generative AI goes a step further by actually producing new data that didn't exist before. For example:Writing articles or stories Creating images or artwork Composing music Writing code Designing products or layouts How It Works Generative AI typically relies on advanced machine learning techniques, especially deep learning models such as:Transformers – used in models like GPT (text) or T5Diffusion models – used in image generation (like DALL·E or Stable Diffusion)GANs (Generative Adversarial Networks) – used for creating realistic mediaA simplified breakdown of the process:Training The model is trained on massive datasets (e.g., books, websites, images, code).It learns statistical patterns, styles, and relationships in the data.Learning Probabilities Instead of memorizing, the model learns the probability of what should come next in a sequence (next word, next pixel, etc.).Generation (Inference)<

DATA SCIENCE with MACHINE LEARNING and DATA ANALYTICS using R Programming, PYTHON Programming, WEKA Tool Kit and SQL. This course is designed for any graduates as well as Software Professionals who are willing to learn data science in simple and easy steps using R programming, Python Programming, WEKA tool kit and SQL.Data is the new Oil. This statement shows how every modern IT system is driven by capturing, storing and analysing data for various needs. Be it about making decision for business, forecasting weather, studying protein structures in biology or designing a marketing campaign. All of these scenarios involve a multidisciplinary approach of using mathematical models, statistics, graphs, databases and of course the business or scientific logic behind the data analysis. So we need a programming language which can cater to all these diverse needs of data science. R and Python shines bright as one such language as it has numerous libraries and built in features which makes it easy to tackle the needs of Data science.In this course we will cover these the various techniques used in data science using the R programming, Python Programming, WEKA tool kit and SQL.The most comprehensive Data Science course in the market, covering the complete Data Science life cycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, programming languages like R programming, Python are covered extensively as part of this Data Science training.

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&nbsp; 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.Requisitos Solo Python básico y ganas de experimentar—el resto (instalación de librerías, datasets y scripts) lo instalamos juntos en el curso

PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks.This comprehensive 2-in-1 course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! Begin with exploring PyTorch and the impact it has made on Deep Learning. Design and implement powerful neural networks to solve some impressive problems in a step-by-step manner. Build a Convolutional Neural Network (CNNs) for image recognition. Also, predict share prices with Recurrent Neural Network and Long Short-Term Memory Network (LSTMs). You’ll learn how to detect credit card fraud with autoencoders and much more! By the end of the course, you’ll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework with the help of real-world examples!Contents and Overview This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with PyTorch, covers building useful and effective deep learning models with the PyTorch Deep Learning framework. In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto-Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you

Complete TensorFlow Mastery For Machine Learning &amp; 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&nbsp; using the TensorFlow framework in Python..&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;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. &nbsp;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&nbsp;IS&nbsp;MY&nbsp;PROMISE&nbsp;TO&nbsp;YOU: COMPLETE&nbsp;THIS&nbsp;ONE&nbsp;COURSE&nbsp;&amp;&nbsp;BECOME&nbsp;A&nbsp;PRO&nbsp;IN&nbsp;PRACTICAL&nbsp;PYTHON&nbsp;TensorFlow BASED&nbsp;DATA&nbsp;SCIENCE!But first things first.&nbsp;My name is&nbsp;Minerva Singh&nbsp;and I&nbsp;am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a&nbsp;PhD at Cambridge University (Tropical Ecology and Conservation). I have several years&nbsp;of&nbsp;experience in analyzing real life data from different sources &nbsp;using data science related techniques and producing publications for international peer reviewed journa

Welcome to this Deep Learning Image Classification course with Py Torch2.0 in Python3. Do you want to learn how to create powerful image classification recognition systems that can identify objects with immense accuracy? if so, then this course is for you what you need! In this course, you will embark on an exciting journey into the world of deep learning and image classification. This hands-on course is designed to equip you with the knowledge and skills necessary to build and train deep neural networks for the purpose of classifying images using the PyTorch framework.We have divided this course into Chapters. In each chapter, you will be learning a new concept for training an image classification model. These are some of the topics that we will be covering in this course:Training all the models with torch.compile which was introduced recently in Pytroch2.0 as a new feature.Install Cuda and Cudnn libraires for Py Torch2.0 to use GPU. How to use Google Colab Notebook to write Python codes and execute code cell by cell.Connecting Google Colab with Google Drive to access the drive data.Master the art of data preparation as per industry standards. Data processing with torchvision library. data augmentation to generate new image classification data by using:- Resize, Cropping, Random Horizontal Flip, Random Vertical Flip, Random Rotation, and Color Jitter.Implementing data pipeline with data loader to efficiently handle large datasets.Deep dive into various model architectures such as Le Net, VGG16, Inception v3, and Res Net50.Each model is explained through a nice block diagram through layer by layer for deeper understanding.Implementing the training and Inferencing pipeline.Understanding transfer learning to train models on less data.Display the model inferencing result

Si estás buscando un curso práctico, completo y avanzado para aprender Machine Learning y Data Science con Big Data utilizando Py Spark, has venido al lugar correcto.Este curso está diseñado para aprender todo lo relacionado con el Machine Learning y Data Science en Spark como modelos de aprendizaje automático de clasificación, regresión, clustering, NLP, Pipelines y técnicas para la ingeniería de datos y preprocesamiento. También te enseñaremos a programar en Py Spark y las buenas prácticas para trabajar con Big Data, visualización de datos o analítica avanzada. Finalmente, aprenderás las últimas tecnologías que han permitido impulsar el Machine learning con Spark como ML Flow, Databricks, Spark ML o Spark Koalas.Este curso es para científicos de datos o aspirantes a científicos de datos que desean obtener capacitación práctica, con las últimas tecnologías y aplicable al mundo real en Py Spark (Python para Apache Spark)El Big Data ha revolucionado el campo del Machine Learning, permitiendo entrenar modelos sobre grandes cantidades de datos. El Machine Learning convencional con Python se ha quedado obsoleto y nuevas tecnologías como Apache Spark han cobrado gran relevancia. Este curso te enseñará todo lo que necesitas saber para posicionarte en el mercado laboral del Machine Learning y aprenderás una de las habilidades más demandadas para ingenieros de datos y científicos de datos.En este curso te enseñaremos todas las habilidades de Machine Learning con Py Spark, partiendo desde las bases hasta las funcionalidades más avanzadas. Para ello utilizaremos presentaciones visu

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 matheux Cependant, 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

Deep learning is not like any other technology, but it is in many cases the only technology that can solve certain problems. We need to ensure that all people involved in the project have a common understanding of what is required, how the process works, and that we have a realistic view of what is possible with the tools at hand. To boil down all this to its core components we could consider a few important rules:create a common ground of understanding, this will ensure the right mindsetstate early how progress should be measuredcommunicate clearly how different machine learning concepts worksacknowledge and consider the inherited uncertainty, it is part of the process In order to define AI, we must first define the concept of intelligence in general. A paraphrased definition based on Wikipedia is:Intelligence can be generally described as the ability to perceive information and retain it as knowledge to be applied towards adaptive behaviors within an environment or context.While there are many different definitions of intelligence, they all essentially involve learning, understanding, and the application of the knowledge learned to achieve one or more goals.Is this course for me?By taking this course, you will gain the tools you need to continue improving yourself in the field of app development. You will be able to apply what you learned to further experience in making your own apps able to perform more.No experience necessary. ​Even if you’ve never coded before, you can take this course. ​​One of the best features is that you can watch the tutorials at any speed you want. This means you can speed up or slow down the video if you want to!When your learning to code, you often find yourself following along with a tutor without really knowing why you're doing certain things. In this course, I will demonstrate correct coding as well as mistakes I often see an

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

Formation Complète Data Science et Machine Learning avec Python Devenez Data Scientist et Maîtrisez l’Apprentissage Automatique avec PythonÊtes-vous prêt à acquérir les compétences les plus recherchées dans la tech et l’analyse de données ? Cette formation complète en Data Science et Machine Learning avec Python vous guidera pas à pas, même si vous partez de zéro, pour devenir un expert capable de transformer des données en décisions stratégiques.Pourquoi choisir cette formation ?Le métier de Data Scientist figure parmi les plus demandés et les mieux rémunérés. Grâce à cette formation unique, vous apprendrez à :Analyser et manipuler des données complexes avec Python.Créer des visualisations impactantes et interactives.Développer et entraîner des modèles prédictifs avancés.Maîtriser les principales bibliothèques Python en Data Science.Un programme complet et progressif Avec plus de 100 vidéos HD, des notebooks Jupyter détaillés, des exemples concrets et des exercices pratiques, vous progresserez étape par étape jusqu’à devenir autonome.Voici un aperçu de ce que vous allez maîtriser :Programmation et traitement des données Programmation avec Python orienté Data Science Manipulation des tableaux numériques avec Num PyGestion et analyse de données tabulaires avec Pandas Lecture et traitement des fichiers CSV et Excel Visualisation de données Création de graphiques professionnels avec Matplotlib Analyse exploratoire et visualisations avancées avec Seaborn Machine Learning supervisé et non supervisé avec Scikit-Lear

Video Learning Path OverviewA Learning Path is a specially tailored course that brings together two or more different topics that lead you to achieve an end goal. Much thought goes into the selection of the assets for a Learning Path, and this is done through a complete understanding of the requirements to achieve a goal.Deep learning is the next step to a more advanced implementation of Machine Learning. Deep Learning allows you to solve problems where traditional Machine Learning methods might perform poorly: detecting and extracting objects from images, extracting meaning from text, and predicting outcomes based on complex dependencies, to name a few.In this practical Learning Path, you will build Deep Learning applications with real-world datasets and Python. Beginning with a step by step approach, right from building your neural nets to reinforcement learning and working with different Deep Learning applications such as computer Vision and voice and image recognition, this course will be your guide in getting started with Deep Learning concepts.Moving further with simple and practical solutions provided, we will cover a whole range of practical, real-world projects that will help customers learn how to implement their skills to solve everyday problems.By the end of the course, you’ll apply Deep Learning concepts and use Python to solve challenging tasks with real-world datasets.Key Features Get started with Deep Learning and build complex models layer by layer, with increasing complexity, in no time.A hands-on guide covering common as well as not-so-common problems in deep learning using Python.Explore the practical essence of Deep Learning in a relatively short amount of time by working on practical, real-world use cases.Author Bios Radhika Datar has more than 6 years' experience in Software Development and Content Writi

Are you ready to embark on a data-driven journey into the world of machine learning and data science? If you're looking for a practical yet powerful starting point, then you're in the right place. Linear regression, the simple yet highly popular machine learning algorithm, is your gateway. It's not just jargon; it's a versatile tool used to uncover crucial insights in your data and predict the future.In this hands-on data science and machine learning project, we'll delve into the driving factors behind California house prices. You'll learn how to clean and visualize data, process it, and harness various Python libraries. By the end of this project, you'll have mastered linear regression in Python and gained essential skills for conducting data science projects.What You'll Gain:Mastery of Python Libraries: Dive into data science and machine learning with pandas, Scikit-Learn, statsmodels, matplotlib, and seaborn.Real-World Application: Apply your knowledge to a hands-on project that you can showcase on your personal website and resume.Step-by-Step Approach: Follow a clear, concise case study to build your confidence and expertise in machine learning and data science.Start your data science journey with a simple yet strong foundation. Let's get started!This course will empower you to unlock the potential of data science, equipping you with the skills to make informed decisions and drive success in the tech industry.

¿Te apetece hacer un curso diferente, en el que no solo aprenderás a dominar todos los pasos de un proyecto de Data Science, sino que también te proporcionará un montón de documentos con toda la teoría y el código que verás en las clases? ¿Te interesa tener una guía, en formato visual y también escrito? Este programa es una mezcla entre el formato de un videocurso tradicional y un máster convencional: está pensado para que, clase a clase, vayas almacenando toda una colección de recursos que, sin duda, se convertirá en tu manual de referencia. Aprenderás a estructurar un proyecto desde cero: sabrás cómo empezar y desarrollar cualquier análisis de datos y conocerás a la perfección todas las herramientas que necesitarás durante el proceso, desde simples funciones de carga de datos, hasta completas librerías de Machine Learning. Además, repasarás todos los conceptos clave de estadística y conocerás cómo funcionan los algoritmos de Machine Learning desde el punto de vista matemático, explicados de una forma gráfica y sencilla. No necesitas tener experiencia previa, ¡pero verás cómo al final del curso te conviertes en todo un experto!A día de hoy, encontrarás:Una colección de más de 30 cuadernos y archivos de Python, totalmente documentados.Documentos en PDF con copia de lo que vamos a ver en las pizarras de trabajo.Recursos y referencias útiles.Trucos, consejos y advertencias de errores que se suelen cometer.Además, tendrás acceso a todas las actualizaciones del curso y a los nuevos recursos que se vayan añadiendo, para siempre.

Este curso sobre el lenguaje de programación R está diseñado para aprender desde cero, paso a paso, hasta convertirte en un experto.Todo está explicado mediante ejemplos para facilitar el aprendizaje Estos son los temas tratados en este curso sobre RConfiguración del entorno Instalación de R y RStudio Introducción a R Operaciones aritméticas, variables, tipos de datos, vectores, operadores de comparación, ayuda y documentación Matrices en R Operaciones aritméticas con matrices, selección de elementos, selección por filas y columnas, función factor Data Frames en R Creación de Data Frames, dataset, selección y ordenación, exportar e importar datos y tratamiento de valores nulos Listas en R Creación y manejo de listas Entrada y salida de datos en R Ficheros CSV, ficheros EXCEL y bases de datos Programación básica de R Operadores lógicos, condicionales if else, bucle while, bucle for y funciones Programación avanzada de R Funciones predefinidas, funciones sobre vectores, funciones anónimas, funciones matemáticas, expresiones regulares, fecha/hora Manipulación de datos con R Manipulación de datos con dplyr, operador pipe y limpieza de datos con tidyr Visualización de datos con R Histogramas, scatterplots, barplots, boxplots, gráficos de distribución, límites y dimensiones Gráficos interactivos con Plotly Introducción a Machine Learning Machine Learning Algoritmo de regresión lineal Algoritmo de regresión logística Algoritmo de los K vecinos más cercanos Algoritmo de árboles de decisión Algo

You're looking for a complete Convolutional Neural Network (CNNs) course that teaches you everything you need to create a Image Recognition model in Python, right?You've found the right Convolutional Neural Networks course!After completing this course you will be able to:Identify the Image Recognition problems which can be solved using CNNs Models.Create CNNs models in Python using Keras and TensorFlow libraries and analyze their results.Confidently practice, discuss and understand Deep Learning concepts Have a clear understanding of Advanced Image Recognition models such as Le Net, Google Net, VGG16 etc.How this course will help you?A Verifiable Certificate of Completion is presented to all students who undertake this Convolutional Neural networks course.If you are an Analyst or an ML scientist, or a student who wants to learn and apply Deep learning in Real world image recognition problems, this course will give you a solid base for that by teaching you some of the most advanced concepts of Deep Learning and their implementation in Python without getting too Mathematical.Why should you choose this course?This course covers all the steps that one should take to create an image recognition model using Convolutional Neural Networks.Most courses only focus on teaching how to run the analysis but we believe that having a strong theoretical understanding of the concepts enables us to create a good model . And after running the analysis, one should be able to judge how good the model is and interpret the results to actually be able to help the business.What makes us qualified to teach you?The course is taught by Abhishek and Pukhraj. As managers in Global Analytics Consulting firm, we have helped businesses solve their business problem using Deep

This video course will get you up-and-running with one of the most cutting-edge deep learning libraries: PyTorch. Written in Python, PyTorch is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs.In this course, you will learn how to accomplish useful tasks using Convolutional Neural Networks to process spatial data such as images and using Recurrent Neural Networks to process sequential data such as texts. You will explore how you can make use of unlabeled data using Auto Encoders. You will also be training a neural network to learn how to balance a pole all by itself, using Reinforcement Learning. Throughout this journey, you will implement various mechanisms of the PyTorch framework to do these tasks.By the end of the video course, you will have developed a good understanding of, and feeling for, the algorithms and techniques used. You'll have a good knowledge of how PyTorch works and how you can use it in to solve your daily machine learning problems.This course uses Python 3.6, and PyTorch 0.3, while not the latest version available, it provides relevant and informative content for legacy users of Python, and PyTorch.About the Author Anand Saha is a software professional with 15 years' experience in developing enterprise products and services. Back in 2007, he worked with machine learning to predict call patterns at TATA Communications. At Symantec and Veritas, he worked on various features of an enterprise backup product used by Fortune 500 companies. Along the way he nurtured his interests in Deep Learning by attending Coursera and Udacity MOO Cs.He is passionate about Deep Learning and its applications; so much so that he quit Veritas at the beginning of 2017 to focus full time on Deep Learning practices. Anand built pipelines to detect and count endangered species from aerial images, trained a robotic arm to pick and place objects, and imp

With the increase of data by each passing day, Data Science has become one of the most important aspects in most of the fields. From healthcare to business, everywhere data is important. However, it revolves around 3 major aspects i.e. data, foundational concepts and programming languages for interpreting the data. This course teaches you everything about all the foundational mathematics for Data Science using R programming language, a language developed specifically for performing statistics, data analytics and graphical modules in a better way.Why Learn Foundational mathematical Concepts for Data Science Using R?Data Science has become an interdisciplinary field which deals with processes and systems used for extracting knowledge or making predictions from large amounts of data. Today, it has become an integral part of numerous fields resulting in the high demand of professionals of data science. From helping brands to understand their customers, solving complex IT problems, to its usability in almost every other field makes it very important for the functioning and growth of any organizations or companies. Depending upon the location the average salary of data scientist expert can be over $120,000. This course will help you learn the concepts the correct way.Why You Should Take This Online Tutorial?Despite the availability of several tutorials on data science, it is one of the online guides containing hand-picked topics on the concepts for foundational mathematics for Data Science using R programming language. It includes myriads of sections (over 9 hours of video content) lectured by Timothy Young, a veteran statistician and data scientists . It explains different concepts in one of the simplest form making the understanding of Foundational mathematics for Data Science very easy and effective.This Course includes:Overview of Machine Learning and R programming language Linear Algebra- Scalars