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Total Size:
176.3 MB
Info Hash:
40814BE0D840FBFDAF6B7B85CA72AF7E5A5DFCC1
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Added:
Sept. 19, 2025, 9:57 a.m.
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(Last updated: Sept. 19, 2025, 9:58 a.m.)
| File | Size |
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| Code.zip | 6.8 MB |
| Chollet F. Deep Learning with Python 3ed 2026.pdf | 169.5 MB |
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176.3 MB
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2025-09-19
| Uploaded by andryold1 | Size 176.3 MB | Health [ 29 /15 ] | Added 2025-09-19 |
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SOURCE: Chollet F. Deep Learning with Python 3ed 2026 Final
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Textbook in PDF format 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. This book was written for anyone who wishes to explore Deep Learning from scratch or broaden their understanding of Deep Learning. Whether you’re a practicing Machine Learning engineer, a software developer, or a college student, you’ll find value in these pages. You’ll explore Deep Learning in an approachable way—starting simply and working up to state-of-the-art techniques. We hope you’ll find that this book strikes a balance between intuition, theory, and hands-on practice. It avoids mathematical notation, preferring instead to explain the core ideas of Deep Learning via functioning code paired with explanations of the underlying principles. You’ll train Machine Learning models from scratch in a number of different problem domains and learn practical recommendations for writing Deep Learning programs and deploying them in the real world. After reading this book, you’ll have a solid understanding of what Deep Learning is, when it’s applicable, and what its limitations are. You’ll be familiar with the standard workflow for approaching and solving Machine Learning problems, and you’ll know how to address commonly encountered issues. 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 With 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 François 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 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 About the reader: For readers with intermediate Python skills. No previous experience with Machine Learning or linear algebra required. But this book can also be valuable to many different types of readers: If you’re a data scientist familiar with machine learning, this book will provide you with a solid, practical introduction to deep learning, the fastest-growing and most significant subfield of machine learning. If you’re a deep learning researcher or practitioner looking to get started with the Keras framework, you’ll find this book to be the ideal Keras crash course. If you’re a graduate student studying deep learning in a formal setting, you’ll find this book to be a practical complement to your education, helping you build intuition around the behavior of deep neural networks and familiarizing you with key best practices. Even technically minded people who don’t code regularly will find this book useful as an introduction to both basic and advanced deep learning concepts. To understand the code examples, you’ll need reasonable Python proficiency. You don’t need previous experience with machine learning or deep learning: this book covers, from scratch, all the necessary basics. You don’t need an advanced mathematics background either—high-school-level mathematics should suffice to follow along. 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. Contents: What is deep learning? The mathematical building blocks of neural networks Introduction to TensorFlow, PyTorch, JAX, and Keras Classification and regression Fundamentals of machine learning The universal workflow of machine learning A deep dive on Keras Image classification ConvNet architecture patterns Interpreting what ConvNets learn Image segmentation Object detection Timeseries forecasting Text classification Language models and the Transformer Text generation Image generation Best practices for the real world The future of AI Conclusions
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