Release date:2021, November 2
Author:Penny de Byl, Penny @Holistic3D.com
Skill level:Beginner
Language:English
Exercise files:Yes
What if you could build a character that could learn while it played? Think about the types of gameplay you could develop where the enemies started to outsmart the player. This is what machine learning in games is all about. In this course, we will discover the fascinating world of artificial intelligence beyond the simple stuff and examine the increasingly popular domain of machines that learn to think for themselves.
In this course, Penny introduces the popular machine learning techniques of genetic algorithms and neural networks using her internationally acclaimed teaching style and knowledge from a Ph.D in game character AI and over 25 years experience working with games and computer graphics. In addition she’s written two award winning books on games AI and two others best sellers on Unity game development. Throughout the course you will follow along with hands-on workshops designed to teach you about the fundamental machine learning techniques, distilling the mathematics in a way that the topic becomes accessible to the most noob of novices.
Learn how to program and work with:
genetic algorithms
neural networks
human player captured training sets
reinforcement learning
Unity’s ML-Agent plugin
Tensorflow
01 Introduction 001 Introduction 002 What is Learning_
02 Genetic Algorithms 005 DNA Inspired Data Structures 006 Camouflage Training with Genetic Algorithms Part 1 007 Camouflage Training with Genetic Algorithms Part 2 008 Camouflage Challenge 009 Coding Movement with Genes Part 1 010 Coding Movement with Genes Part 2 011 Distance Challenge 012 Moving GAs with Senses Part 1 013 Moving GAs with Senses Part 2 014 Moving GAs with Senses Part 3 015 Maze Walking Challenge 016 Maze Walking Challenge Solution Part 2 017 Not So Flappy Birds Part 1 018 Not So Flappy Birds Part 2
03 Perceptrons_ The making of a Neural Network 020 The Perceptron 022 Programming and Training a Perceptron 025 Perceptron Classification 026 Perceptron Learning from Experience 027 Saving Loading Perceptron Values
04 Artificial Neural Networks 028 Introduction to Neural Networks 029 Programming An Artificial Neural Network Part 1 030 Programming An Artificial Neural Network Part 2 031 Programming An Artificial Neural Network Part 3 032 ANN FAQs 033 Working with Activation Functions
05 Neural Networks in Practice 036 Developing a Neural Network that Plays Pong Part 1 037 Developing a Neural Network that Plays Pong Part 2 038 Developing a Neural Network that Plays Pong Part 3 040 Gathering Training Data from the Player Part 1 041 Gathering Training Data from the Player Part 2 042 Training with Player Data Part 1 044 Training with Player Data Part 2 045 Training with Player Data Part 3
06 Reinforcement Learning with the Q-Network 046 Reinforcement Learning and Q-Networks 047 Training a Neural Network with Q-Learning Part 1 048 Training a Neural Network with Q-Learning Part 2 049 Training a Neural Network with Q-Learning Part 3 050 Challenge
07 Unitys Machine Learning Agents and TensorFlow 055 An Overview of ML-Agents 057 Creating an ML-Agent From Scratch Part 1 058 Creating an ML-Agent From Scratch Part 2 060 An Avoiding ML-Agent Part 1 061 An Avoiding ML-Agent Part 2 063 Top 10 Tips for Neural Network Best Practice 064 Environment Sensing ML-Agent 065 Goal Seeking Wall Jumping Part 1 066 Goal Seeking Wall Jumping Part 2
08 A Final Word 068 Thank you
[Udemy] A Beginners Guide To Machine Learning with Unity_Subtitles.7z [Udemy] A Beginners Guide To Machine Learning with Unity.7z
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