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Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras.
Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
Artigo n.º: 71532216

Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition

Artigo n.º: 71532216

STD 1573445

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Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras.
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O que se Destaca

Comprehensive Guide
This edition covers supervised, unsupervised, and reinforcement learning, providing a holistic understanding for beginners and experienced practitioners alike, enhancing skills in diverse AI applications.
Hands-On Projects
Engage in practical projects that reinforce learning, ensuring users gain real-world experience in building and deploying deep learning models using TensorFlow and Keras.
Updated Content
The third edition features the latest advancements in TensorFlow and Keras, ensuring that users are equipped with contemporary knowledge and tools to stay competitive in the rapidly evolving AI field.

Detalhes do produto

Find the latest edition of Deep Learning with TensorFlow and Keras at Ubuy São Tomé and Príncipe. Build, deploy, and explore various machine learning models for your projects.
  • Learn deep learning and machine learning fundamentals with clear explanations and extensive code samples
  • Implement graph neural networks, transformers, and joint and contrastive learning using TensorFlow and Keras
  • Explore cutting-edge machine and deep learning techniques for cloud, mobile, and large production environments
  • Understand the latest TensorFlow 2.x features, intuitive APIs based on Keras, and flexible model building on any platform
  • Create and deploy neural networks with TensorFlow, covering popular algorithms such as CNNs, GANs, RNNs, NLP, and GNNs
  • Target audience includes Python developers and data scientists aiming to build machine learning and deep learning systems
Publisher Packt Publishing
Publication date October 6, 2022
Edition 3rd ed.
Language English
Print length 698 pages
ISBN-10 1803232919
ISBN-13 978-1803232911
Item Weight 2.6 pounds (1.18 kg)
Dimensions 7.5 x 1.58 x 9.25 inches (19.1 x 4 x 23.5 cm)

Quem Deverá Comprar?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners seeking to learn deep learning fundamentals using TensorFlow and Keras effectively.

  • Machine Learning Practitioners

    Beneficial for professionals wanting to expand their knowledge in deep learning applications and model deployment.

  • Academic Researchers

    Useful for academics looking to implement and experiment with various deep learning models in research projects.

Not Suitable For
  • Complete Beginners

    Those with no programming or mathematical background might struggle to grasp advanced concepts quickly.

DESCRIÇÃO DO PRODUTO

Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition

About This Item

Explore the fascinating world of deep learning with the comprehensive "Deep Learning with TensorFlow and Keras" 3rd Edition. Whether you're a beginner or an experienced practitioner, this book serves as an invaluable resource for building and deploying supervised, unsupervised, deep, and reinforcement learning models. This edition provides practical guidance, tutorials, and in-depth discussions on advanced techniques, making it a must-have for anyone delving into TensorFlow and Keras.

Elevate your understanding and mastery of deep learning models with this essential textbook.

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Expert Systems Editorial Review

The "Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition" offers a comprehensive overview of machine learning methods but is marred by numerous typos throughout the content, specifically in equations and model results. The poor print quality of the book and its sheer weight make handling it cumbersome, prompting the suggestion to divide it into multiple volumes. Furthermore, the book assumes a pre-existing familiarity with neural networks, forgoing detailed explanations of concepts and resorting to complex glossaries without sufficient background information. However, the extensive code samples and examples are lauded as invaluable resources for learning deep learning systems, particularly for individuals new to the field. While the book's thorough coverage of machine and deep learning concepts is appreciated, the lack of detail and vague explanations have been critiqued. **

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Prós

  • Offers a wide array of machine learning methods
  • Extensive code samples and examples
  • Thorough coverage of machine and deep learning concepts
  • A valuable resource for individuals new to the field

Contras

  • Littered with typos, particularly in equations and model results

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