Neural Networks in Unity
C# Programming for Windows 10
Nonfiction, Computers, Entertainment & Games, Game Programming - Graphics, General Computing, Programming
| Author: | Abhishek Nandy, Manisha Biswas | ISBN: | 9781484236734 |
| Publisher: | Apress | Publication: | July 14, 2018 |
| Imprint: | Apress | Language: | English |
| Author: | Abhishek Nandy, Manisha Biswas |
| ISBN: | 9781484236734 |
| Publisher: | Apress |
| Publication: | July 14, 2018 |
| Imprint: | Apress |
| Language: | English |
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 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.
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 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.
