Deep Learning with Python, Third Edition

Chollet Francois (Autor)

Buch | Softcover

648 Seiten

2026 | 3rd edition
Manning Publications (Verlag)
978-1-63343-658-9 (ISBN)

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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.

Why wait to master deep learning when the tools are already within reach? As AI reshapes every industry, learning how to build models like GPT or generate stunning images is no longer a luxury—it is a necessity. Many developers struggle with fragmented tutorials or outdated libraries, leaving them unsure how to translate theory into practice. What if you could gain hands-on experience with the latest tools and techniques, backed by expert guidance, and build models that deliver real results from the start?

First-principles walkthroughs: Understand every layer, activation, and optimizer, so troubleshooting feels natural.

Keras 3 showcase: Use the newest API features for faster, cleaner model pipelines.

Multiframework primer: Compare TensorFlow, PyTorch, and JAX to pick the right tool every time.

Generative AI chapters: Craft text with your own GPT-style model and create images using diffusion.

Production guidance: Learn scaling, tuning, and deployment tips that move notebooks into real apps.

Deep Learning with Python, Third Edition, by François Chollet and Matthew Watson, delivers an authoritative, code-first roadmap from the minds behind Keras.

Each chapter builds knowledge step by step, pairing intuitive explanations with color-coded listings you can run immediately. Expanded coverage tackles transformers, diffusion, and hardware-friendly workflows while retaining the approachable tone that made previous editions bestsellers.

By the final page you will confidently architect, train, and fine-tune state-of-the-art models, ready to solve vision, language, and forecasting problems in your own projects.

Ideal for developers with intermediate Python skills who crave practical, future-proof AI expertise.

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.

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

Erscheinungsdatum	11.11.2025
Verlagsort	New York
Sprache	englisch
Themenwelt	Schulbuch / Wörterbuch ► Wörterbuch / Fremdsprachen
	Geisteswissenschaften ► Sprach- / Literaturwissenschaft ► Sprachwissenschaft
	Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge
	Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
ISBN-10	1-63343-658-6 / 1633436586
ISBN-13	978-1-63343-658-9 / 9781633436589
Zustand	Neuware