Deep Learning with Python, Third Edition
Deep Learning with Python, Third Edition
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Deep Learning with Python, Third Edition
Deep Learning with Python, Third Edition
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
Nº de artículo: 183016583

Deep Learning with Python, Third Edition

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Nº de artículo: 183016583

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Lo que destaca

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.

Detalles de producto

Shop Deep Learning with Python, Third Edition online at a best price in México. 1633436586
  • 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 principlesThe latest features of Keras 3A primer on JAX, PyTorch, and TensorFlowImage classification and image segmentationTime series forecastingLarge Language modelsText classification and machine translationText and image generation—build your own GPT and diffusion models!Scaling and tuning modelsWith 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 technologyIn 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 bookDeep 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 insideHands-on, code-first learningComprehensive, from basics to generative AIIntuitive and easy math explanationsExamples in Keras, PyTorch, JAX, and TensorFlowAbout the readerFor readers with intermediate Python skills. No previous experience with machine learning or linear algebra required.About the authorFrançois 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 Contents1 What is deep learning?2 The mathematical building blocks of neural networks3 Introduction to TensorFlow, PyTorch, JAX, and Keras4 Classification and regression5 Fundamentals of machine learning6 The universal workflow of machine learning7 A deep dive on Keras8 Image classification9 ConvNet architecture patterns10 Interpreting what ConvNets learn11 Image segmentation12 Object detection13 Timeseries forecasting14 Text classification15 Language models and the Transformer16 Text generation17 Image generation18 Best practices for the real world19 The future of AI20 Conclusions
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)

¿Quién debería comprarlo?

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.

DESCRIPCIÓN DEL PRODUCTO

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Preguntas y respuestas de los clientes

  • pregunta: What new features are included in the third edition of Deep Learning With Python?

    respuesta: The third edition introduces updates reflecting the latest advancements in deep learning, including improved explanations of Keras and TensorFlow, updated models, and practical tips for creating robust applications. The authors delve into recent research findings and practical implementations, enabling readers to grasp the nuances of building deep learning models. For instance, if you're looking to apply neural networks to real-world problems like image classification or natural language processing, this edition offers clearer examples and case studies to enhance your understanding.
  • pregunta: Is this book suitable for beginners in deep learning?

    respuesta: Yes, Deep Learning With Python, Third Edition, is designed to be accessible for beginners. The book starts with fundamental concepts in deep learning and gradually builds up to more advanced topics, ensuring that readers can follow along without prior experience. For example, if you're new to programming, you will find the explanations helpful as they break down complex ideas into digestible parts, making it feasible to learn deep learning in a practical context.
  • pregunta: What programming knowledge do I need to read this book?

    respuesta: Readers should have a basic understanding of Python, as the book leverages the language for implementing deep learning models. While prior knowledge of machine learning concepts can be beneficial, it is not a strict requirement. The author provides codes and insights that help you understand how to apply Python libraries effectively. If you have experience running simple scripts or working on small projects in Python, you'll be well equipped to dive into the examples presented.
  • pregunta: Are there practical examples included in this edition?

    respuesta: Absolutely, the third edition is filled with practical examples that illustrate the application of deep learning concepts. These examples provide hands-on experience using Keras and TensorFlow, allowing readers to implement models from scratch. For instance, readers can expect to work through projects like creating chatbots or classifying images, providing a direct pathway to apply theoretical knowledge in real-world scenarios.
  • pregunta: How does the book address the challenges in deep learning?

    respuesta: The book addresses challenges such as overfitting, data scarcity, and implementation complexity by incorporating best practices gleaned from the authors' experiences. It explains techniques like dropout, data augmentation, and regularization to mitigate these issues. For example, if you're struggling with model performance, the strategies outlined will help you refine your approach and optimize results, ultimately leading to more effective model training.
  • pregunta: Will this book help me understand neural networks?

    respuesta: Yes, this book provides extensive coverage of neural networks, from basic architectures to advanced techniques. It explains fundamental concepts like layers, activation functions, and training algorithms in a clear manner. If you're aspiring to build neural networks for projects such as image recognition or data prediction, this book will offer the necessary conceptual tools and hands-on practices to equip you with the skills needed.
  • pregunta: Is there a focus on practical applications of deep learning?

    respuesta: The third edition emphasizes practical applications throughout, featuring case studies and examples relevant to a variety of industries. It explores how deep learning can be applied in areas like healthcare, finance, and autonomous driving. Consequently, if you are looking to implement AI solutions within your sector, this book will provide insights on how to leverage deep learning effectively in real-world applications.
  • pregunta: What level of detail does this book go into regarding TensorFlow and Keras?

    respuesta: This edition delves deeply into TensorFlow and Keras, providing both conceptual insights and practical coding examples. Readers will learn how to leverage these powerful libraries for building and training neural networks effectively. If you are looking to develop complex models, you will find sections that guide you on optimizing performance and ensuring robustness for production-ready solutions.
  • pregunta: Can I find resources online to complement the book?

    respuesta: Yes, the book often points readers to online resources, including code repositories, tutorials, and forums, enhancing the learning experience. These additional resources are ideal for deepening your understanding, allowing you to engage with a community of learners and industry professionals. If you want to explore beyond the text, these supplementary materials will support your journey into deep learning.
  • pregunta: Where can I buy Deep Learning With Python, Third Edition in Mexico?

    respuesta: You can purchase Deep Learning With Python, Third Edition at Ubuy, which offers a reliable platform for obtaining the book. Ubuy provides an efficient shopping experience, allowing you to browse and buy with confidence, ensuring you receive your copy seamlessly. Check Ubuy today for the availability of this essential resource in your area.

Neural Networks Editorial Review

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**** 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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Ventajas

  • 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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Características y beneficios

  • Comprehensive guide to deep learning with practical examples.
  • Covers latest features of Keras 3, TensorFlow, PyTorch, and JAX.
  • Includes innovative topics like generative AI and large language models.
  • Designed for intermediate Python users, no prior ML experience required.
  • Clear explanations and intuitive visuals for effective learning.
  • Over 100,000 copies sold, trusted by developers and data scientists.

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