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You're reading from Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms

Product type Paperback

Published in Dec 2019

Publisher Packt

ISBN-13 9781838824914

Length 368 pages

Edition 1st Edition

Languages

Python

Concepts

Neural Networks

Author (1):

Iaroslav Omelianenko

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Table of Contents (18) Chapters

Preface

1. Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods FREE CHAPTER

2. Overview of Neuroevolution Methods

3. Python Libraries and Environment Setup

4. Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems

5. Using NEAT for XOR Solver Optimization

6. Pole-Balancing Experiments

7. Autonomous Maze Navigation

8. Novelty Search Optimization Method

9. Section 3: Advanced Neuroevolution Methods

10. Hypercube-Based NEAT for Visual Discrimination

11. ES-HyperNEAT and the Retina Problem

12. Co-Evolution and the SAFE Method

13. Deep Neuroevolution

14. Section 4: Discussion and Concluding Remarks

15. Best Practices, Tips, and Tricks

16. Concluding Remarks

17. Other Books You May Enjoy

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Python coding tips and tricks

Having decided to work with Python, it is vital to learn the best coding practices of the language. Here, we provide you with some tips and give you directions to continue with self-learning.

Coding tips and tricks

The following coding tips and tricks will help you master Python:

You should learn how to use the popular machine learning libraries, such as NumPy (https://numpy.org), pandas (https://pandas.pydata.org), and Scikit-learn (https://scikit-learn.org/stable/). Mastering these libraries will give you tremendous power in data manipulation and analysis. This will help you to avoid many mistakes, and enable easy debugging of the results collected from the experiments.
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Authors (1)

Omelianenko

Iaroslav Omelianenko occupied the position of CTO and research director for more than a decade. He is an active member of the research community and has published several research papers at arXiv, ResearchGate, Preprints, and more. He started working with applied machine learning by developing autonomous agents for mobile games more than a decade ago. For the last 5 years, he has actively participated in research related to applying deep machine learning methods for authentication, personal traits recognition, cooperative robotics, synthetic intelligence, and more. He is an active software developer and creates open source neuroevolution algorithm implementations in the Go language.

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