Deep Learning with TensorFlow and Keras – Third Edition

Deep Learning with TensorFlow and Keras, 3rd Edition

eBook Details:

  • Paperback: 698 pages
  • Publisher: WOW! eBook; 3rd edition (October 6, 2022)
  • Language:¬†English
  • ISBN-10: 1803232919
  • ISBN-13: 978-1803232911

eBook Description:

Deep Learning with TensorFlow and Keras, 3rd Edition: Build cutting edge machine and deep learning systems for the lab, production, and mobile devices

Deep Learning with TensorFlow and Keras, Third Edition 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.

  • 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

This Deep Learning with TensorFlow and Keras, 3rd Edition 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.

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