Francois Chollet – författare

DESCRIPTIONDeep learning is applicable to a widening range of artificialintelligence problems, such as image classification, speech recognition,text classification, question answering, text-to-speech, and opticalcharacter recognition.

Deep Learning with Python is structured around a series of practicalcode examples that illustrate each new concept introduced anddemonstrate best practices. By the time you reach the end of this book,you will have become a Keras expert and will be able to apply deeplearning in your own projects.

KEY FEATURES

• Practical code examples• In-depth introduction to Keras• Teaches the difference between Deep Learning and AI

ABOUT THE TECHNOLOGYDeep learning is the technology behind photo tagging systems atFacebook and Google, self-driving cars, speech recognition systems onyour smartphone, and much more.

AUTHOR BIOFrancois Chollet is the author of Keras, one of the most widely usedlibraries for deep learning in Python. He has been working with deep neuralnetworks since 2012. Francois is currently doing deep learning research atGoogle. He blogs about deep learning at blog.keras.io.

Get the eBook free when you register your print book at Manning.This book introduces deep learning from scratch with examples that use the R language and the Keras library. Each chapter offers practical code examples that build your understanding of deep learning layer by layer. Youll appreciate the intuitive explanations, crisp illustrations, and clear examples. In this expanded third edition youll find fresh chapters on the transformers architecture, building your own GPT-like large language model, and image generation with diffusion models. Plus, even DL veterans will benefit from the insightful explanations on the nature of deep learning.For R programmers, the R interface to the Keras deep learning library is a powerful head start on building deep learning models without switching to Python. It provides a simple, consistent API that makes deep learning accessible and simplifies the process of building neural networks, even if you have no prior experience in advanced machine learning.In Deep Learning with R, Third Edition you will learn: Deep learning from first principles The latest features of Keras Image classification and image segmentation Time series forecasting Text classification and machine translation Text and image generationbuild your own LLMs and diffusion models! Scaling and tuning modelsAbout the technologyDeep Learning with R, Third Edition is a practical, concept-driven introduction to modern deep learning for R users. With a focus on clarity, intuition, and hands-on experimentation, it guides you from the foundations of deep learning to advanced architectures such as transformers and LLMs. This book treats R as a fully capable environment for modern deep learning, showing how contemporary models and workflows can be developed end to end without compromise.About the bookDeep Learning with R, Third Edition gets you up to speed with the current state of deep learning practice. Using Keras 3 with R, youll build and train neural networks from scratch, work with transformers, fine-tune pretrained models and explore large language models and diffusion-based image generation. By following carefully constructed examples that build insight step-by-step, youll develop a deep understanding of why these models worknot just how to use them.What's inside Hands-on, code-first learning in R A clear progression from deep learning fundamentals to generative AI Examples that emphasize intuition and understandingAbout the readerFor readers with intermediate R skills. No prior experience with deep learning is required.About the authorFranois Chollet is the creator of Keras and author of Deep Learning with Python. Tomasz Kalinowski is a software engineer at Posit Software, PBC and maintainer of the Keras and TensorFlow R packages.Table of Contents1 What is deep learning?2 The mathematical building blocks of neural networks3 Introduction to TensorFlow, PyTorch, JAX, and Keras4 Classification and regression5 Fundamentals of machine learning6 The universal workflow of machine learning7 A deep dive into Keras8 Image classification9 Convnet architecture patterns10 Interpreting what convnets learn11 Image segmentation12 Object detection13 Timeseries forecasting14 Text classification15 Language models and the Transformer16 Text generation17 Image generation18 Best practices for the real world19 The future of AI20 Conclusions

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R.

 

Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.

Deploying computer vision, audio, and natural language processing in the browser Fine-tuning machine learning models with client-side data Constructing and training a neural network Interactive AI for browser games using deep reinforcement learning Generative neural networks to generate music and pictures

TensorFlow.js is an open-source JavaScript library for defining, training, and deploying deep learning models to the web browser. It’s quickly  gaining  popularity with developers for its amazing set of benefits including scalability, responsiveness, modularity, and portability.

 

Shanging Cai and Eric Nielsen are senior software engineers on the Google Brain team.

 

Stan Bileschi is the technical lead for Google’s TensorFlow Usability team, which built the TensorFlow Layers API. All three have advanced degrees from MIT. Together, they’re responsible for writing most of TensorFlow.js.

Deep learning from the ground up using R and the powerful Keras library! In   Deep Learning with R, Second Edition  you will learn: Deep learning from first principles Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition  shows you how to put deep learning into action. It's based on the revised new edition of François Chollet's bestselling   Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. about the technology

Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. what's inside

Image classification and image segmentation Time series forecasting Text classification and machine translation Text generation, neural style transfer, and image generation about the reader

For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required.  

Unlock the groundbreaking advances of deep learning with this extensively revised edition of the bestselling original. Learn directly from the creator of Keras and master practical Python deep learning techniques that are easy to apply in the real world.In Deep Learning with Python, Second Edition you will learn:     Deep learning from first principles     Image classification & image segmentation     Timeseries forecasting     Text classification and machine translation     Text generation, neural style transfer, and image generation Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised second edition introduces deep learning using Python and Keras, and is loaded with insights for both novice and experienced ML practitioners. You’ll learn practical techniques that are easy to apply in the real world, and important theory for perfecting neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Recent innovations in deep learning unlock exciting new software capabilities like automated language translation, image recognition, and more. Deep learning is becoming essential knowledge for every software developer, and modern tools like Keras and TensorFlow put it within your reach, even if you have no background in mathematics or data science.  About the book Deep Learning with Python, Second Edition introduces the field of deep learning using Python and the powerful Keras library. In this new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners. As you move through this book, you’ll build your understanding through intuitive explanations, crisp illustrations, and clear examples. You’ll pick up the skills to start developing deep-learning applications. What's inside     Deep learning from first principles     Image classification and image segmentation     Time series forecasting     Text classification and machine translation     Text generation, neural style transfer, and image generation About the reader For readers with intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the author François Chollet is a software engineer at Google and creator of the Keras deep-learning library. Table of Contents 1  What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for timeseries 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions

Deep learning from the ground up using R and the powerful Keras library!In Deep Learning with R, Second Edition you will learn:     Deep learning from first principles     Image classification and image segmentation     Time series forecasting     Text classification and machine translation     Text generation, neural style transfer, and image generation Deep Learning with R, Second Edition shows you how to put deep learning into action. It’s based on the revised new edition of François Chollet’s bestselling Deep Learning with Python. All code and examples have been expertly translated to the R language by Tomasz Kalinowski, who maintains the Keras and Tensorflow R packages at RStudio. Novices and experienced ML practitioners will love the expert insights, practical techniques, and important theory for building neural networks. About the technology Deep learning has become essential knowledge for data scientists, researchers, and software developers. The R language APIs for Keras and TensorFlow put deep learning within reach for all R users, even if they have no experience with advanced machine learning or neural networks. This book shows you how to get started on core DL tasks like computer vision, natural language processing, and more using R. About the book Deep Learning with R, Second Edition is a hands-on guide to deep learning using the R language. As you move through this book, you’ll quickly lock in the foundational ideas of deep learning. The intuitive explanations, crisp illustrations, and clear examples guide you through core DL skills like image processing and text manipulation, and even advanced features like transformers. This revised and expanded new edition is adapted from Deep Learning with Python, Second Edition by François Chollet, the creator of the Keras library. What's inside     Image classification and image segmentation     Time series forecasting     Text classification and machine translation     Text generation, neural style transfer, and image generation About the reader For readers with intermediate R skills. No previous experience with Keras, TensorFlow, or deep learning is required. About the author François Chollet is a software engineer at Google and creator of Keras. Tomasz Kalinowski is a software engineer at RStudio and maintainer of the Keras and Tensorflow R packages. J.J. Allaire is the founder of RStudio, and the author of the first edition of this book. Table of Contents 1 What is deep learning? 2 The mathematical building blocks of neural networks 3 Introduction to Keras and TensorFlow 4 Getting started with neural networks: Classification and regression 5 Fundamentals of machine learning 6 The universal workflow of machine learning 7 Working with Keras: A deep dive 8 Introduction to deep learning for computer vision 9 Advanced deep learning for computer vision 10 Deep learning for time series 11 Deep learning for text 12 Generative deep learning 13 Best practices for the real world 14 Conclusions

SummaryDeep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. The book builds your understanding of deep learning through intuitive explanations and practical examples. Continue your journey into the world of deep learning with Deep Learning with R in Motion, a practical, hands-on video course available exclusively at Manning.com (www.manning.com/livevideo/deep-​learning-with-r-in-motion).Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyMachine learning has made remarkable progress in recent years. Deep-learning systems now enable previously impossible smart applications, revolutionizing image recognition and natural-language processing, and identifying complex patterns in data. The Keras deep-learning library provides data scientists and developers working in R a state-of-the-art toolset for tackling deep-learning tasks.About the BookDeep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and adapted for R by RStudio founder J. J. Allaire, this book builds your understanding of deep learning through intuitive explanations and practical examples. You''ll practice your new skills with R-based applications in computer vision, natural-language processing, and generative models.What''s InsideDeep learning from first principlesSetting up your own deep-learning environmentImage classification and generationDeep learning for text and sequencesAbout the ReaderYou''ll need intermediate R programming skills. No previous experience with machine learning or deep learning is assumed.About the AuthorsFrançois Chollet is a deep-learning researcher at Google and the author of the Keras library.J.J. Allaire is the founder of RStudio and the author of the R interfaces to TensorFlow and Keras.Table of Contents

PART 1 - FUNDAMENTALS OF DEEP LEARNING

What is deep learning?Before we begin: the mathematical building blocks of neural networksGetting started with neural networksFundamentals of machine learning

PART 2 - DEEP LEARNING IN PRACTICE

Deep learning for computer visionDeep learning for text and sequencesAdvanced deep-learning best practicesGenerative deep learningConclusions

SummaryDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About the TechnologyMachine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn''t beat a serious Go player, to defeating a world champion. Behind this progress is deep learning—a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications.About the BookDeep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You''ll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you''ll have the knowledge and hands-on skills to apply deep learning in your own projects. What''s InsideDeep learning from first principlesSetting up your own deep-learning environment Image-classification modelsDeep learning for text and sequencesNeural style transfer, text generation, and image generationAbout the ReaderReaders need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required.About the AuthorFrançois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others.Table of Contents

PART 1 - FUNDAMENTALS OF DEEP LEARNING

What is deep learning?Before we begin: the mathematical building blocks of neural networks Getting started with neural networksFundamentals of machine learning

PART 2 - DEEP LEARNING IN PRACTICE

Deep learning for computer visionDeep learning for text and sequencesAdvanced deep-learning best practicesGenerative deep learningConclusionsappendix A - Installing Keras and its dependencies on Ubuntuappendix B - Running Jupyter notebooks on an EC2 GPU instance

The bestselling book on Python deep learning, now covering generative AI, Keras 3, PyTorch, and JAX!Deep Learning with Python, Third Edition puts the power of deep learning in your hands. This new edition includes the latest Keras and TensorFlow features, generative AI models, and added coverage of PyTorch and JAX. Learn directly from the creator of Keras and step confidently into the world of deep learning with Python.In Deep Learning with Python, Third Edition youll discover:Deep learning from first principlesThe latest features of Keras 3A primer on JAX, PyTorch, and TensorFlowImage classification and image segmentationTime series forecastingLarge Language modelsText classification and machine translationText and image generationbuild your own GPT and diffusion models!Scaling and tuning modelsWith over 100,000 copies sold, Deep Learning with Python makes it possible for developers, data scientists, and machine learning enthusiasts to put deep learning into action. In this expanded and updated third edition, Keras creator Franois Chollet offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images.About the technologyIn less than a decade, deep learning has changed the worldtwice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator Franois Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider.About the bookDeep Learning with Python, Third Edition makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer.What's insideHands-on, code-first learningComprehensive, from basics to generative AIIntuitive and easy math explanationsExamples in Keras, PyTorch, JAX, and TensorFlowAbout the readerFor readers with intermediate Python skills. No previous experience with machine learning or linear algebra required.About the authorFranois Chollet is the co-founder of Ndea and the creator of Keras. Matthew Watson is a software engineer at Google working on Gemini and a core maintainer of Keras.Table of Contents1 What is deep learning?2 The mathematical building blocks of neural networks3 Introduction to TensorFlow, PyTorch, JAX, and Keras4 Classification and regression5 Fundamentals of machine learning6 The universal workflow of machine learning7 A deep dive on Keras8 Image classification9 ConvNet architecture patterns10 Interpreting what ConvNets learn11 Image segmentation12 Object detection13 Timeseries forecasting14 Text classification15 Language models and the Transformer16 Text generation17 Image generation18 Best practices for the real world19 The future of AI20 Conclusions

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesImplement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learningLearn cutting-edge machine and deep learning techniquesBook DescriptionDeep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (GANs), recurrent neural networks (RNNs), natural language processing (NLP), and graph neural networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.What you will learnLearn how to use the popular GNNs with TensorFlow to carry out graph mining tasksDiscover the world of transformers, from pretraining to fine-tuning to evaluating themApply self-supervised learning to natural language processing, computer vision, and audio signal processingCombine probabilistic and deep learning models using TensorFlow ProbabilityTrain your models on the cloud and put TF to work in real environmentsBuild machine learning and deep learning systems with TensorFlow 2.x and the Keras APIWho this book is forThis hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.Some machine learning knowledge would be useful. We don't assume TF knowledge.