Hands-On Neuroevolution with Python
This book will help you to apply popular neuroevolution strategies to existing neural network designs to improve their performance. It covers practical examples in areas such as games, robotics, and simulation of natural processes, using real-world examples and data sets for your better understanding.
Offered by
Difficulty Level
Intermediate
Completion Time
12h16m
Language
English
About Book
Who Is This Book For?
This book is for machine learning practitioners, deep learning researchers, and AI enthusiasts who are looking to implement neuroevolution algorithms from scratch. Working knowledge of the Python programming language and basic knowledge of deep learning and neural networks are mandatory.
Hands-On Neuroevolution with Python
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 12h16m total length
Overview of Neuroevolution Methods
Python Libraries and Environment Setup
Using NEAT for XOR Solver Optimization
Pole-Balancing Experiments
Autonomous Maze Navigation
Novelty Search Optimization Method
Hypercube-Based NEAT for Visual Discrimination
ES-HyperNEAT and the Retina Problem
Co-Evolution and the SAFE Method
Deep Neuroevolution
Best Practices, Tips, and Tricks
Concluding Remarks
Related Resources
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