Deep Learning with Python, Third Edition
кас 2025 · Simon and Schuster
Электронная кніга
648
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Пра гэту электронную кнігу
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 you’ll discover:
About the technology
In less than a decade, deep learning has changed the world—twice. 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 François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider.
About the book
Deep 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 inside
For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required.
About the author
François 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 Contents
1 What is deep learning?
2 The mathematical building blocks of neural networks
3 Introduction to TensorFlow, PyTorch, JAX, and Keras
4 Classification and regression
5 Fundamentals of machine learning
6 The universal workflow of machine learning
7 A deep dive on Keras
8 Image classification
9 ConvNet architecture patterns
10 Interpreting what ConvNets learn
11 Image segmentation
12 Object detection
13 Timeseries forecasting
14 Text classification
15 Language models and the Transformer
16 Text generation
17 Image generation
18 Best practices for the real world
19 The future of AI
20 Conclusions
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 you’ll discover:
- Deep learning from first principles
- The latest features of Keras 3
- A primer on JAX, PyTorch, and TensorFlow
- Image classification and image segmentation
- Time series forecasting
- Large Language models
- Text classification and machine translation
- Text and image generation—build your own GPT and diffusion models!
- Scaling and tuning models
About the technology
In less than a decade, deep learning has changed the world—twice. 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 François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider.
About the book
Deep 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 inside
- Hands-on, code-first learning
- Comprehensive, from basics to generative AI
- Intuitive and easy math explanations
- Examples in Keras, PyTorch, JAX, and TensorFlow
For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required.
About the author
François 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 Contents
1 What is deep learning?
2 The mathematical building blocks of neural networks
3 Introduction to TensorFlow, PyTorch, JAX, and Keras
4 Classification and regression
5 Fundamentals of machine learning
6 The universal workflow of machine learning
7 A deep dive on Keras
8 Image classification
9 ConvNet architecture patterns
10 Interpreting what ConvNets learn
11 Image segmentation
12 Object detection
13 Timeseries forecasting
14 Text classification
15 Language models and the Transformer
16 Text generation
17 Image generation
18 Best practices for the real world
19 The future of AI
20 Conclusions
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 coverag...
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Звесткі пра аўтара
François Chollet is a software engineer at Google and creator of Keras.
Matthew Watson is a core maintainer of the Keras deep learning library, focusing primarily on tools for Natural Language Processing.
Matthew Watson is a core maintainer of the Keras deep learning library, focusing primarily on tools for Natural Language Processing.
François Chollet is a software engineer at Google and creator of Keras.
Matthew Watson is a core maintainer of the Keras deep learning library, focusing primarily on tools for Natural Language Processing.
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