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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: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition

Deep Learning with TensorFlow and Keras: Build and deploy supervised,
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Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition Paperback - 2022

by Amita Kapoor; Antonio Gulli; Sujit Pal

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Packt Publishing. 3rd ed.. Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.
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Details

  • Title Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models, 3rd Edition
  • Author Amita Kapoor; Antonio Gulli; Sujit Pal
  • Binding Paperback
  • Edition 3rd ed.
  • Condition Used - Very good
  • Pages 698
  • Volumes 1
  • Language ENG
  • Publisher Packt Publishing
  • Publication date 2022-10-06
  • Bookseller's Inventory # 1803232919-11-1
  • ISBN 9781803232911 / 1803232919
  • Weight 2.6 lbs (1.18 kg)
  • Dimensions 9.25 x 7.5 x 1.4 in (23.50 x 19.05 x 3.56 cm)
  • Category Computers - General Information
  • Quantity available 1

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

From the publisher

Build cutting edge machine and deep learning systems for the lab, production, and mobile devices.


Purchase of the print or Kindle book includes a free eBook in PDF format.


Key Features:

  • Understand the fundamentals of deep learning and machine learning through clear explanations and extensive code samples
  • Implement graph neural networks, transformers using Hugging Face and TensorFlow Hub, and joint and contrastive learning
  • Learn cutting-edge machine and deep learning techniques


Book Description:

Deep Learning with TensorFlow and Keras teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You'll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.


TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.


This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, GANs, recurrent neural networks (RNNs), natural language processing (NLP), and Graph Neural Networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.


What You Will Learn:

  • Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasks
  • Discover the world of transformers, from pretraining to fine-tuning to evaluating them
  • Apply self-supervised learning to natural language processing, computer vision, and audio signal processing
  • Combine probabilistic and deep learning models using TensorFlow Probability
  • Train your models on the cloud and put TF to work in real environments
  • Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API


Who this book is for:

This hands-on machine learning book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow, and AutoML to build machine learning systems.


Some machine learning knowledge would be useful. We don't assume TF knowledge.

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