Advanced Deep Learning

Comprehensive deep learning specialization by Andrew Ng. Master neural networks, CNNs, RNNs, and modern deep learning architectures.

Master TensorFlow 2 and Keras. ANNs, CNNs, RNNs, GANs, deployment.

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

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

                     YOUR COMPLETE GUIDE TO H2O: POWERFUL R PACKAGE FOR MACHINE LEARNING, & DEEP LEARNING IN R    This course covers the main aspects of the H2O package for data science in R. If you take this course, you can do away with taking other courses or buying books on R based data science as you will have the keys to a very powerful R supported data science framework.  In this age of big data, companies across the globe use R to sift through the avalanche of information at their disposal. By becoming proficient in machine learning, neural networks and deep learning via a powerful framework, H2O in R, you can give your company a competitive edge and boost your career to the next level!LEARN FROM AN EXPERT DATA SCIENTIST:My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment), graduate. I finished a PhD at Cambridge University, UK, where I specialized in data science models. I have +5 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 R data science courses and books out there do not account for the multidimensional nature of the topic. This course will give you a robust grounding in the main aspects of practical neural networks and deep learning. Unlike other R instructors, I dig deep into the data science features of R and give you a one-of-a-kind grounding in data science... You will go all the way from carrying out data reading & cleaning to finally implementing powerful neural networks and deep learning algorithms and evaluating their performance using R.Among other things:You will be introduced to powerful R-based

This course was designed with the support of AI to provide an improved learning.Transform yourself from someone who struggles with AI buzzwords into a confident Natural Language Processing expert who understands both the foundational science and cutting-edge innovations that power today's AI revolution. This comprehensive course, developed with AI assistance, takes you on a complete journey from classical linguistics to the Transformer architecture behind ChatGPT, BERT, and every modern language model.Master the Complete NLP Pipeline From Classical Methods to Modern AI• Build rock-solid foundations with computational linguistics, morphology, and semantic analysis• Implement classic algorithms like TF-IDF, Hidden Markov Models, and Part-of-Speech tagging• Understand the revolutionary shift from RNNs to Transformers and why attention mechanisms changed everything• Decode the science behind BERT, GPT, and how RLHF makes AI assistants helpful and harmless• Navigate the ethical implications of bias in language models with practical mitigation strategies• Explore cutting-edge multimodal AI where vision meets language in models like CLIP and LLaVA• Grasp the geopolitical landscape of AI development, from data sovereignty to the global "chip war"This isn't just another coding tutorial – it's your complete guide to understanding how machines truly comprehend human language.The demand for NLP expertise has exploded by 400% over the past 3 years, with companies desperately seeking professionals who understand both the technical foundations and practical applications. While others struggle with surface-level tutorials, you'll gain deep comprehension of the underlying mechanisms that drive a $43 billion industry. The pressure to implement AI soluti

More and more evidence has demonstrated that graph representation learning especially graph neural networks (GN Ns) has tremendously facilitated computational tasks on graphs including both node-focused and graph-focused tasks. The revolutionary advances brought by GN Ns have also immensely contributed to the depth and breadth of the adoption of graph representation learning in real-world applications. For the classical application domains of graph representation learning such as recommender systems and social network analysis, GN Ns result in state-of-the-art performance and bring them into new frontiers. Meanwhile, new application domains of GN Ns have been continuously emerging such as combinational optimization, physics, and healthcare. These wide applications of GN Ns enable diverse contributions and perspectives from disparate disciplines and make this research field truly interdisciplinary.In this course, I will start by talking about basic graph data representation and concepts like node data, edge types, adjacency matrix and Laplacian matrix etc. Next, we will talk about broad kinds of graph learning tasks and discuss basic operations needed in a GNN: filtering and pooling. Further, we will discuss details of different types of graph filtering (i.e., neighborhood aggregation) methods. These include graph convolutional networks, graph attention networks, confidence GC Ns, Syntactic GC Ns and the general message passing neural network framework. Next, we will talk about three main types of graph pooling methods: Topology based pooling, Global pooling and Hierarchical pooling. Within each of these three types of graph pooling methods, we will discuss popular methods. For example, in topology pooling we will talk about Normalized Cut and Graclus mainly. In Global pooling, we will talk about Set2Set and Sort Pool. In Hierarchical pooling, we will talk about diff Pool, g Pool and SAG Pool. Next, we will talk about three unsupervised graph neural network architectures: Graph

Schon mal darüber nachgedacht, wie große Sprachmodelle (LL Ms) die Welt verändern und beispiellose Chancen schaffen?"KI wird deinen Job nicht übernehmen, aber jemand, der weiß, wie man KI nutzt, könnte es tun," sagt Richard Baldwin.Bist du bereit, die Feinheiten von LL Ms zu meistern und ihr volles Potenzial für verschiedene Anwendungen zu nutzen, von Datenanalyse bis zur Erstellung von Chatbots und KI-Agenten?Dann ist dieser Kurs für dich!Tauche ein in 'LLM Mastery: OpenAI, Gemini, Claude, Llama, ChatGPT &amp; AP Is'—wo du die grundlegenden und fortgeschrittenen Konzepte von LL Ms, ihre Architekturen und praktischen Anwendungen erforschen wirst. Verändere dein Verständnis und deine Fähigkeiten, um die Führung in der KI-Revolution zu übernehmen.Dieser Kurs ist perfekt für Entwickler, Datenwissenschaftler, KI-Enthusiasten und alle, die an der Spitze der Technologie von LL Ms stehen möchten. Egal ob du neuronale Netzwerke verstehen, KI-Modelle feinabstimmen oder KI-gesteuerte Anwendungen entwickeln möchtest, dieser Kurs bietet dir alles, was du brauchst.Was dich in diesem Kurs erwartet:Umfassendes Wissen über LL Ms:Verständnis von LL Ms: Lerne über Parameter, Gewichte, Inferenz und neuronale Netze.Neuronale Netzwerke: Verstehe die Funktionsweise neuronaler Netze mit Tokens in LL Ms.Transformer-Architektur: Erforsche die Transformer-Architektur und Mixture of Experts.Feinabstimmung: Verstehe den Prozess der Feinabstimmung und die Entwicklung des Assistant-Modells</stron

This course contains the use of artificial intelligence.The generative AI revolution is here, but the path from excitement to enterprise value is riddled with costly missteps, regulatory landmines, and technology dead ends. You're not just competing with yesterday's processes anymore - you're racing against organizations that have cracked the code on generative AI for business productivity while others burn through budgets chasing shiny AI promises. The difference between AI success and AI failure isn't about having the latest technology - it's about having the strategic framework to implement it profitably.Master Strategic AI Implementation That Actually Delivers ROI• Analyze real case studies from companies achieving 40% productivity gains with ChatGPT and Claude• Calculate true Total Cost of Ownership for API vs self-hosted AI models• Navigate EU AI Act compliance and data sovereignty requirements• Build advanced RAG systems that leverage your private company data securely• Implement multi-agent frameworks for autonomous business workflows• Measure and prove AI ROI using enterprise-grade metrics and frameworks Stop Guessing. Start Leading with Data-Driven AI Strategy.Why mastering generative AI for business is your competitive imperative right now. Research from Mc Kinsey shows that 75% of enterprises plan to adopt generative AI within 24 months, but only 23% have clear strategies for implementation and ROI measurement. The companies moving first are already capturing massive advantages - Git Hub reported 55% faster code completion with Copilot, while Klarna achieved $40 million in annual savings through AI customer service automation.The pressure is real. Your competitors are either already implementing AI or desperately trying to catch up. Meanwhile, regulatory frameworks like the EU AI Act are creating compliance require

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

Python est reconnu comme l'un des meilleurs langages de programmation pour sa flexibilité. Il fonctionne dans presque tous les domaines, du développement Web au développement d'applications financières. Cependant, ce n'est un secret pour personne que la meilleure application de Python est dans les tâches d'apprentissage automatique, d'apprentissage en profondeur et d'intelligence artificielle.Bien que Python facilite l'utilisation du Machine Learning et du Deep Learning, il sera toujours assez frustrant pour quelqu'un qui n'a aucune connaissance du fonctionnement de l'apprentissage automatique.Si vous connaissez les bases de Python et que vous avez envie d'apprendre le Deep Learning, ce cours est fait pour vous. Ce cours vous aidera à apprendre à créer des programmes qui acceptent la saisie de données et automatisent l'extraction de fonctionnalités, simplifiant ainsi les tâches du monde réel pour les humains.Il existe des centaines de ressources d'apprentissage automatique disponibles sur Internet. Cependant, vous risquez d'apprendre des leçons inutiles si vous ne filtrez pas ce que vous apprenez. Lors de la création de ce cours, nous avons tout filtré pour isoler les bases essentielles dont vous aurez besoin dans votre parcours d'apprentissage en profondeur.C'est un cours de base qui convient aussi bien aux débutants qu'aux experts. Si vous êtes à la recherche d'un cours qui commence par les bases et passe aux sujets avancés, c'est le meilleur cours pour vous.Il enseigne uniquement ce dont vous avez besoin pour vous lancer dans l'apprentissage automatique et l'apprentissage en profondeur sans fioritures. Bien que cela aide à garder le cours assez concis, il s'agit de tout ce dont vous avez besoin pour commencer avec le sujet.

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

you will learn all these Topics and lot more 1. Core Concepts1. Perceptron2. MLPs and its Notation3. Forward Propagation4. Backpropagation5. Chain Rule of Derivative in Backpropagation6. Vanishing Gradient Problem7. Exploding Gradient Activation Functions List of Activation Functions1. Linear Function2. Binary Step Function3. Sigmoid Function (Logistic Function)4. Tanh (Hyperbolic Tangent Function)5. ReLU (Rectified Linear Unit)6. Leaky ReLU7. Parametric ReLU (PReLU)8. Exponential Linear Unit (ELU)9. Scaled Exponential Linear Unit (SELU)10. Softmax11. Swish.12. Soft Plus13. Mish14. Maxout15. GELU (Gaussian Error Linear Unit)16. SiLU (Sigmoid Linear Unit)17. Gated Linear Unit (GLU)18. SwiGLU19. Mish Activation Function Derivative of Activation Functions Properties of Activation Functions1. Saturating vs Non-Saturating2. Smooth vs Non-Smooth3. Generalized vs Specialized4. Underflow and Overflow5. Undefined and Defined6. Computationally Expensive vs Inexpensive.7. 0-Centered and Non-0-Centered8. Differentiable vs Non-Differentiable9. Bounded and Unbounded10. Monotonicity11. Linear Vs Non Linear Ideal Activation Function Characteristics1. Non-Linearity2. Differentiability3. Computational Efficiency4. Avoids Saturation5. Non-Sparse (Dense) Gradients6. Centered Output (0-Centered)7. Prevents Exploding Gradients8. Monotonicity (Optional)9. Sparse Activations (Optional)1

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

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 AP Is.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 Author Vlad 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

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

Este taller equipa a los líderes empresariales para impulsar iniciativas de IA, y posteriormente entregar e implementar soluciones de IA, generando cambios en toda la organización con un impacto comercial medible.¡Esto es muy diferente a un “curso” tradicional!De hecho, no es un curso como tal: es un informe ejecutivo. Un briefing integral y orientado a la acción sobre IA Generativa, diseñado por líderes y para líderes.Lo que cubriremos Experiencia en IA desde una perspectiva comercial Casos de uso reales: tanto historias de éxito como fracasos Kits de herramientas accionables para aplicar en tu negocio Ejemplos desde startups en stealth mode hasta empresas globales Lo que NO cubriremos Detalles técnicos profundos (pero sí lo suficiente para apoyar la toma de decisiones).Ejemplo: abordaremos RAG, fine-tuning y agentes, pero siempre desde un punto de vista empresarial.Uso directo de herramientas de IA por parte del alumno.Este informe trata sobre cómo transformar tu organización para que use herramientas de IA, no sobre el uso individual de cada una.Si eres un ejecutivo, emprendedor o líder (o estás en el camino de convertirte en uno), este briefing te colocará en una posición estratégica para alcanzar el éxito comercial con la IA generativa.Lo que aprenderás Estrategia de IA, toma de decisiones en IA y liderazgo en IA.Este taller desarrolla tu expertise a través de 3 módulos:Módulo 1: C

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 PyTorch:&nbsp;Deep Learning and Artificial Intelligence!Although Google's Deep Learning library TensorFlow has gained massive popularity over the past few years, PyTorch has been the library of choice for professionals and researchers around the globe for deep learning and artificial intelligence.Is it possible that TensorFlow is popular only because Google is popular and used effective marketing?Why did TensorFlow change so significantly between version 1 and version 2?&nbsp;Was there something deeply flawed with it, and are there still potential problems?It is less well-known that PyTorch is backed by another Internet giant, Facebook (specifically, the Facebook AI&nbsp;Research Lab - FAIR). So if you want a popular deep learning library backed by billion dollar companies and lots of community support, you can't go wrong with PyTorch. And maybe it's a bonus that the library won't completely ruin all your old code when it advances to the next version. ;)On the flip side, it is very well-known that all the top AI shops (ex. OpenAI, Apple, and JP Morgan Chase) use PyTorch. OpenAI just recently switched to PyTorch in 2020, a strong sign that PyTorch is picking up steam.If you are a professional, you will quickly recognize that building and testing new ideas is extremely easy with PyTorch, while it can be pretty hard in other libraries that try to do everything for you. Oh, and it's faster.Deep Learning has been responsible for some amazing achievements recently, such as:<ul

Máster Especialista de Deep Learning en Python con PyTorch.Redes Neuronales Profundas con PyTorch: Diseño, Implementación y Evaluación de Modelos Neuronales desde 0 a experto.Instructor: PhD. Manuel Castillo-Cara Requisitos previos: Se recomienda tener conocimientos sobre Machine Learning. Se recomienda realizar previamente siguiente curso de Udemy:Machine Learning con Python. Aprendizaje Automático Avanzado - Aprendizaje Automático Scikit-Learn en Python. Modelos Predictivos. Data Science. De básico a Experto.Descripción del Curso:Bienvenido al curso de Deep Learning con Python y PyTorch. En este curso exploraremos a fondo la librería PyTorch de Python para Deep Learning, aprendiendo cómo utilizarla para desarrollar y evaluar modelos de Deep Learning avanzados. Nuestro objetivo es proporcionarte las técnicas, el código y las habilidades necesarias para que puedas aplicar el Deep Learning en tus propios proyectos innovadores.PyTorch se ha convertido en una de las herramientas más potentes y flexibles en el campo del aprendizaje profundo. A diferencia de otras librerías, PyTorch ofrece un enfoque dinámico y intuitivo para la construcción de redes neuronales, permitiéndote definir y modificar tus modelos con gran facilidad.En este curso, nos centraremos en el desarrollo práctico de modelos de Deep Learning utilizando PyTorch. Comenzaremos con los fundamentos y avanzaremos hacia técnicas más sofisticadas, permitiéndote construir una base sólida que podrás expandir en el futuro según tus necesidades y proyectos específicos.Hemos elegido PyTorch como nuestra plataforma principal debido a su capacidad para desarrollar rápidamente modelos de Deep Learning potentes y eficientes. PyTorch combina la potencia de la computación GPU con una API intuitiva, lo que nos permitir

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

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