Deep Learning with Python, Third Edition
Deep Learning with Python, Third Edition puts the power of deep learning in your hands.
Deep Learning with Python, Third Edition
Numéro d'article: 183016583

Deep Learning with Python, Third Edition

Numéro d'article: 183016583

€ 87

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Ce qui se démarque

Comprehensive Coverage
This edition offers extensive insights into deep learning, covering fundamental concepts to advanced techniques, ensuring a thorough understanding for both novices and experienced programmers alike.
Hands-On Learning
With practical examples and real-world applications, readers can quickly implement deep learning techniques, bridging the gap between theory and practical application effectively.
Updated Content
This third edition includes the latest developments in deep learning frameworks and libraries, providing readers with the current state-of-the-art tools and practices.

Détails du produit

Shop Deep Learning with Python, Third Edition online at a best price in Saint Pierre and Miquelon. 1633436586
Publisher Manning
Publication date November 18, 2025
Edition 3rd
Language English
Print length 648 pages
ISBN-10 1633436586
ISBN-13 978-1633436589
Item Weight 1.58 pounds (720 grams)
Dimensions 7.38 x 1.6 x 9.25 inches (18.7 x 4.1 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • Students and Beginners

    Ideal for those new to deep learning concepts and seeking a structured, accessible introduction.

  • Professionals Transitioning

    Suitable for software engineers or data scientists looking to transition into deep learning or enhance their skills.

  • Practical Project Developers

    Great for practitioners wanting hands-on experience with building neural networks and practical applications.

Not Suitable For
  • Advanced Experts

    Not suitable for seasoned deep learning researchers or experts seeking advanced theoretical content and methodologies.

DESCRIPTION DU PRODUIT

Deep Learning with Python, Third Edition

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What prior knowledge do I need to understand this book?

    répondre: You need intermediate Python skills, but no prior machine learning or deep learning experience is required.
  • question: What topics are covered in the third edition?

    répondre: The third edition covers generative AI, transformers, diffusion models, image and text generation, and modern best practices.
  • question: Who is the author of the book?

    répondre: The book is authored by François Chollet, the creator of Keras.

Neural Networks Editorial Review

**** The third edition of "Deep Learning with Python" by François Chollet has garnered widespread acclaim as an essential resource for anyone looking to delve into the world of AI and machine learning. Many readers have praised the book for its captivating and well-structured approach, making it the go-to manual for foundational knowledge in the field. From the onset, the text successfully explains fundamental concepts in machine learning and deep learning, laying a solid groundwork before venturing into more complex themes from Chapter 12 onwards. This chapter and those that follow introduce new material reflective of advancements in technology up until the mid-to-late 2020s, including critical discussions on object detection and large language models (LLMs). The blend of clear explanations and practical, executable Python code creates an enriching experience, appealing to both novices and experienced developers. Readers have pointed out the author's effective teaching methods that combine theoretical understanding with real-world methodologies. As noted, the book does not shy away from in-depth best practices, making it particularly beneficial for beginners entering the field. The comprehensive range of topics allows readers to access essential subjects like text classification, diffusion models, and generative AI methods all in one volume. Additionally, the choice of coding examples across popular frameworks such as TensorFlow, JAX, and PyTorch aligns well with current industry practices. This versatility ensures the material is not only informative but also relevant in today’s evolving tech landscape. The quality of the printing and digital formats has been highlighted as well, further enhancing the overall reading experience. Overall, this edition stands out as an indispensable tool for developers and learners alike, merging insightful content with hands-on applications, confirming its position as a top resource in the realm of machine learning literature. **

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Avantages

  • Well-structured and captivating content.
  • Clear explanations of fundamental and advanced topics.
  • Practical examples with Python code facilitate learning.
  • Comprehensive coverage of major themes, including recent advancements in technology.
  • Includes coding examples in popular frameworks such as TensorFlow, JAX, and PyTorch.
  • Engaging writing style makes complex concepts accessible.

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