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Description
Learn the basics of neural networks and explore different types of neural networks with Unity as a platform. In this book, we begin by exploring backpropagation and unsupervised neural networks using Unity and C#. It then moves on to activation functions such as sigma functions, step functions, etc. The author also explains all variations of neural networks, such as feedforward, recurrent, and radial.
Once you’ve got the basics down, you can start programming Unity with C#. In this section, the author discusses the construction of neural networks for unsupervised learning, the representation of neural networks in C# in terms of data structures, and the replication of a neural network as a simulation in the Unity program. Finally, you need to define backpropagation using Unity C# before building your project.
What will you learn?
Explore the concepts behind neural networks
Work with Unity and C#
See the difference between fully connected and convolutional neural networks
Core Neural Network Processing for Windows 10 UWP
Who is this book for?
Game experts, machine learning and deep learning enthusiasts
