Neural Networks in Unity
C# Programming for Windows 10
Book Description:
Learn the core concepts of neural networks and discover the different types of neural network, using Unity as your platform. In this Neural Networks in Unity 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 Learn
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
Who This Book Is For
Gaming professionals, machine learning and deep learning enthusiasts.
Neural Networks in Unity.
Описание книги:
В этой книге вы начнете с изучения обратного распространения и неконтролируемых нейронных сетей с Unity и C#. Затем вы перейдете к функциям активации, таким как сигмоидные функции, функции шага и т. д. Изучите основные понятия нейронных сетей и откройте для себя различные типы нейронных сетей, используя Unity в качестве своей платформы. После изучения основ, вы начнете программировать в Unity на C#. Рассмотрено построение нейронных сетей для неконтролируемого обучения.
Представлена нейронная сеть в терминах структур данных C# и смоделирована как нейронная сеть в Unity. Наконец, перед компиляцией проекта необходимо определить обратное распространение с помощью Unity C#. Автор также объясняет все вариации нейронных сетей, такие как feed forward, recurrient и radial.
- Chapter 1: Neural Network Basics
Introducing Neural Networks
Digging Deeper into Neural Networks
Perceptron
Activation Function and Its Different Types
Biases and Weights
Neural Network from Scratch
Backpropagation
Summary - Chapter 2: Unity ML-Agents
Unity IDE
Getting Started with Machine Learning Agents
Internal Operations for Machine Learning - Chapter 3: Machine Learning Agents and Neural Network in Unity
Extending the Unity ML-Agents with Further Examples
Crawler Project
Testing the Simulation
Neural Network with Unity C#
Creating DataStructures
Experimenting with the Spider Asset
Summary - Chapter 4: Backpropagation in Unity
Going Further into Backpropagation
Backpropogation in Unity C#
Constructing Data Structures
Feed Forwarding and Initializing Weights
Testing of Backpropagation Neural Network
Summary - Chapter 5: Data Visualization in Unity
Machine Learning Data Visualization in Unity
Data Parsing
Working with Datasets
Another Example
Summary
Index
| From external source | link |
| Source Code | link |
| mirror | link |
Referring to wikipedia.org, you can view a list of well-known games using the Unity engine version.
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