Master reinforcement learning, a popular area of machine learning, starting with the basics: discover how agents and the environment evolve and then gain a clear picture of how they are inter-related. You’ll then work with theories related to reinforcement learning and see the concepts that build up the reinforcement learning process. Reinforcement Learning discusses algorithm implementations important for reinforcement learning, including Markov’s Decision process and Semi Markov Decision process. The next section shows you how to get started with Open AI before looking at Open AI Gym. You’ll then learn about Swarm Intelligence with Python in terms of reinforcement learning. The last part of the book starts with the TensorFlow environment and gives an outline of how reinforcement learning can be applied to TensorFlow. There’s also coverage of Keras, a framework that can be used with reinforcement learning. Finally, you'll delve into Google’s Deep Mind and see scenarios where reinforcement learning can be used. What You'll Learn 
<ul>
<li>Absorb the core concepts of the reinforcement learning process 
</li>
<li>Use advanced topics of deep learning and AI 
</li>
<li>Work with Open AI Gym, Open AI, and Python </li>
<li>Harness reinforcement learning with TensorFlow and Keras using Python</li>
</ul>
 Who This Book Is ForData scientists, machine learning and deep learning professionals, developers who want to adapt and learn reinforcement learning.

Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python

Computers & Technology

Nandy, Abhishek

Biswas, Manisha

Computer Science

Apress

Learn the core concepts of neural networks and discover the different types of neural network, using Unity as your platform. In this book you will start by exploring back propagation and unsupervised neural networks with Unity and C#. You’ll then move onto activation functions, such as sigmoid functions, step functions, and so on. The author also explains all the variations of neural networks such as feed forward, recurrent, and radial.Once you’ve gained the basics, you’ll start programming Unity with C#. In this section the author discusses constructing neural networks for unsupervised learning, representing a neural network in terms of data structures in C#, and replicating a neural network in Unity as a simulation. Finally, you’ll define back propagation with Unity C#, before compiling your project.What You'll LearnDiscover the concepts behind neural networksWork with Unity and C# See the difference between fully connected and convolutional neural networksMaster neural network processing for Windows 10 UWPWho This Book Is ForGaming professionals, machine learning and deep learning enthusiasts.

Neural Networks in Unity: C# Programming for Windows 10

Operating Systems

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Books by Biswas, Manisha