A Beginner's Guide To Machine Learning with Unity

A Beginner's Guide To Machine Learning with Unity
A Beginner's Guide To Machine Learning with Unity
MP4 | Video: 1280x720 | Duration: 12 Hours | 2.1 GB | Subtitles: VTT | Sources
Author: Penny de Byl | Language: English | Skill level: Intermediate

Advanced games AI with genetic algorithms, neural networks & Q-learning in C# and Tensorflow for Unity !

The course starts with a thorough examination of genetic algorithms that will ease you into one of the simplest machine learning techniques that is capable of extraordinary learning. You'll develop an agent that learns to camouflage, a flappy bird inspired application in which the birds learn to make it through a maze and environment sensing bots that learn to stay on a platform.

Following this you'll dive right into creating your very own neural network in C# from scratch. With this basic neural network you will find out how to train behaviour, capture and use human player data to train an agent and teach a bot to drive. In the same section you'll have the Q-learning algorithm explained before integrating it into your own applications.

By this stage you'll feel confident with the terminology and techniques used throughout the deep learning community and ready to tackle Unity's experimental ML-Agents. Together with Tensorflow you'll be throwing agents in the deep end and reinforcing their knowledge to stay alive in a variety of game environment scenarios.

By the end of the course you'll have a well equiped toolset of basic and solid machine learning algorithms and applications that will see you able to decipher the latest research publications and integrate the latest developments into your work while keeping abreast of Unity's ML-Agents as they evolve from experimental to production release.

You should be familiar with the Unity Game Engine.
You should have a working knowledge of C#.
You should have a healthy appreciation for mathematics and statistics.

A Beginner's Guide To Machine Learning with Unity

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