Deep Learning with TensorFlow and Keras - Third Edition
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Deep Learning with TensorFlow and Keras - Third Edition

Build and deploy supervised, unsupervised, deep, and reinforcement learning models

About the Book

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, generative adversarial networks (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.

About the Author

Packt Publishing Ltd
Packt Publishing Ltd

Packt Publishing are an established global technical learning content provider, founded in Birmingham, UK with over twenty years’ experience in delivering premium rich content from ground-breaking authors on a wide range of emerging and popular technologies. Our titles have global relevance our multimedia portfolio includes over 9,000 books, e-books, audiobooks and video courses. www.packtpub.com

Table of Contents

  1. Neural Networks Foundations with TF
  2. Regression and Classification
  3. Convolutional Neural Networks
  4. Word Embeddings
  5. Recurrent Neural Network
  6. Transformers
  7. Unsupervised Learning
  8. Autoencoders
  9. Generative Models
  10. Self-Supervised Learning
  11. Reinforcement Learning
  12. Probabilistic TensorFlow
  13. An Introduction to AutoML
  14. The Math Behind Deep Learning
  15. Tensor Processing Unit
  16. Other Useful Deep Learning Libraries
  17. Graph Neural Networks
  18. Machine Learning Best Practices
  19. TensorFlow 2 Ecosystem
  20. Advanced Convolutional Neural Networks

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