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Neural Networks in Unity

C# Programming for Windows 10

  • Abhishek Nandy
  • Manisha Biswas

Table of contents

  1. Front Matter
    Pages i-xi
  2. Abhishek Nandy, Manisha Biswas
    Pages 1-26
  3. Abhishek Nandy, Manisha Biswas
    Pages 27-67
  4. Abhishek Nandy, Manisha Biswas
    Pages 69-111
  5. Abhishek Nandy, Manisha Biswas
    Pages 113-136
  6. Abhishek Nandy, Manisha Biswas
    Pages 137-154
  7. Back Matter
    Pages 155-158

About this book

Introduction

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.

You will:
  • Discover the concepts behind neural networks
  • Work with Unity and C# 
  • See the difference between fully connected and convolutional neural networks
  • Master neural network processing for Windows 10 UWP

Keywords

Unity Artificial intelligence Neural Networks Back Propogation C# Conventional Neural Networks

Authors and affiliations

  • Abhishek Nandy
    • 1
  • Manisha Biswas
    • 2
  1. 1.KolkataIndia
  2. 2.North 24 ParganasIndia

Bibliographic information