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Deep Learning with Python, Third Edition de Chollet Francois

Deep Learning with Python, Third Edition: Manning Publications

Autor Chollet Francois
en Limba Engleză Paperback – 22 dec 2025

Prin parcurgerea acestui volum, cititorul va dobândi capacitatea de a construi de la zero arhitecturi complexe, de la sisteme de clasificare a imaginilor la modele de limbaj de tip GPT și rețele de difuzie pentru generarea imaginilor. Subliniem că Deep Learning with Python, Third Edition nu este doar o actualizare de rutină, ci o recalibrare a întregului ecosistem de învățare profundă, fiind scrisă chiar de Chollet Francois, creatorul bibliotecii Keras. Remarcăm flexibilitatea noii ediții, care depășește granițele TensorFlow pentru a integra Keras 3 cu JAX și PyTorch, oferind astfel o perspectivă agnostică față de framework-ul utilizat. Abordarea diferă de Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow de Aurélien Géron prin focalizarea intensă pe fundamentele rețelelor neuronale și pe intuiția matematică din spatele straturilor, fiind mai puțin un manual general de machine learning și mai mult un ghid specializat de deep learning aplicat. Reținem fluiditatea cu care autorul trece de la conceptele de bază la aplicații avansate, precum prognoza seriilor temporale, segmentarea imaginilor și procesarea limbajului natural prin arhitecturi Transformers. Stilul narativ este unul tehnic, dar extrem de accesibil, în care fiecare concept este ancorat în exemple de cod executabile, eliminând abstractizarea excesivă în favoarea implementării directe. Spre deosebire de alte titluri tehnice, această lucrare publicată de Manning Publications reușește să explice nu doar „cum” să implementezi un model, ci și „de ce” anumite arhitecturi funcționează mai bine în contexte specifice de producție.

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Specificații

ISBN-13: 9781633436589
ISBN-10: 1633436586
Pagini: 648
Dimensiuni: 184 x 232 x 36 mm
Greutate: 1.26 kg
Ediția:3. Auflage
Editura: PEARSON EDUCATION LTD
Colecția Manning Publications
Seria Manning Publications

De ce să citești această carte

Recomandăm această carte oricărui dezvoltator cu cunoștințe medii de Python care dorește să stăpânească inteligența artificială generativă. Veți câștiga o înțelegere profundă a Keras 3 și veți învăța să construiți propriile modele GPT și sisteme de difuzie direct de la sursă. Este resursa definitivă pentru a trece de la teorie la implementări de stat-of-the-art în AI.

Descriere

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: • 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 book Deep Learning with Python, Third Edition introduces deep learning from scratch. Each chapter introduces practical code examples that build up your understanding of deep learning layer by layer. You’ll appreciate the intuitive explanations, crisp color illustrations, and clear examples. In this expanded third edition you’ll 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. 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. Matthew Watson is a core maintainer of the Keras deep learning library, focusing primarily on tools for Natural Language Processing. Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.

Descriere scurtă

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:
  • 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.

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