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Python Deep Learning

Python Deep Learning

By : Zocca, Spacagna, Daniel Slater, Roelants
4.1 (10)
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Python Deep Learning

Python Deep Learning

4.1 (10)
By: Zocca, Spacagna, Daniel Slater, Roelants

Overview of this book

With an increasing interest in AI around the world, deep learning has attracted a great deal of public attention. Every day, deep learning algorithms are used broadly across different industries. The book will give you all the practical information available on the subject, including the best practices, using real-world use cases. You will learn to recognize and extract information to increase predictive accuracy and optimize results. Starting with a quick recap of important machine learning concepts, the book will delve straight into deep learning principles using Sci-kit learn. Moving ahead, you will learn to use the latest open source libraries such as Theano, Keras, Google's TensorFlow, and H20. Use this guide to uncover the difficulties of pattern recognition, scaling data with greater accuracy and discussing deep learning algorithms and techniques. Whether you want to dive deeper into Deep Learning, or want to investigate how to get more out of this powerful technology, you’ll find everything inside.
Table of Contents (12 chapters)
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11
Index

Actor-critic methods


Approaches to reinforcement learning can be divided into three broad categories:

  • Value-based learning: This tries to learn the expected reward/value for being in a state. The desirability of getting into different states can then be evaluated based on their relative value. Q-learning in an example of value-based learning.

  • Policy-based learning: In this, no attempt is made to evaluate the state, but different control policies are tried out and evaluated based on the actual reward from the environment. Policy gradients are an example of that.

  • Model-based learning: In this approach, which will be discussed in more detail later in the chapter, the agent attempts to model the behavior of the environment and choose an action based on its ability to simulate the result of actions it might take by evaluating its model.

Actor-critic methods all revolve around the idea of using two neural networks for training. The first, the critic, uses value-based learning to learn a value function...

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