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Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras - Second Edition

By : Antonio Gulli, Dr. Amita Kapoor, Sujit Pal
4.3 (26)
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Deep Learning with TensorFlow 2 and Keras

Deep Learning with TensorFlow 2 and Keras

4.3 (26)
By: Antonio Gulli, Dr. Amita Kapoor, Sujit Pal

Overview of this book

Deep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside 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 is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before. This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
Table of Contents (19 chapters)
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17
Other Books You May Enjoy
18
Index

Reinforcement Learning

This chapter introduces reinforcement learning (RL)—the least explored and yet most promising learning paradigm. Reinforcement learning is very different from both supervised and unsupervised learning models we have done in earlier chapters. Starting from a clean slate (that is, having no prior information), the RL agent can go through multiple stages of hit and trials, and learn to achieve a goal, all the while the only input being the feedback from the environment. The latest research in RL by OpenAI seems to suggest that continuous competition can be a cause for the evolution of intelligence. Many deep learning practitioners believe that RL will play an important role in the big AI dream: Artificial General Intelligence (AGI). This chapter will delve into different RL algorithms, the following topics will be covered:

  • What is RL and its lingo
  • Learn how to use OpenAI Gym interface
  • Deep Q-Networks
  • Policy gradients
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
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Deep Learning with TensorFlow 2 and Keras
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