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Hands-On Neuroevolution with Python
Hands-On Neuroevolution with Python

Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms

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Profile Icon Omelianenko
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₱2500.99
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
Paperback Dec 2019 368 pages 1st Edition
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Paperback
₱2500.99
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Arrow left icon
Profile Icon Omelianenko
Arrow right icon
₱2500.99
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3 (1 Ratings)
Paperback Dec 2019 368 pages 1st Edition
eBook
₱1399.99 ₱2000.99
Paperback
₱2500.99
Subscription
Free Trial
eBook
₱1399.99 ₱2000.99
Paperback
₱2500.99
Subscription
Free Trial

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Hands-On Neuroevolution with Python

Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods

This section introduces core concepts of evolutionary computation and discusses particulars of neuroevolution-based algorithms and which Python libraries can be used to implement them. You will become familiar with the fundamentals of neuroevolution methods and will get practical recommendations on how to start your experiments. This section provides a basic introduction to the Anaconda package manager for Python as part of your environment setup.

This section comprises the following chapters:

  • Chapter 1, Overview of Neuroevolution Methods
  • Chapter 2, Python Libraries and Environment Setup
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Key benefits

  • Implement neuroevolution algorithms to improve the performance of neural network architectures
  • Understand evolutionary algorithms and neuroevolution methods with real-world examples
  • Learn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulations

Description

Neuroevolution is a form of artificial intelligence learning that uses evolutionary algorithms to simplify the process of solving complex tasks in domains such as games, robotics, and the simulation of natural processes. This book will give you comprehensive insights into essential neuroevolution concepts and equip you with the skills you need to apply neuroevolution-based algorithms to solve practical, real-world problems. You'll start with learning the key neuroevolution concepts and methods by writing code with Python. You'll also get hands-on experience with popular Python libraries and cover examples of classical reinforcement learning, path planning for autonomous agents, and developing agents to autonomously play Atari games. Next, you'll learn to solve common and not-so-common challenges in natural computing using neuroevolution-based algorithms. Later, you'll understand how to apply neuroevolution strategies to existing neural network designs to improve training and inference performance. Finally, you'll gain clear insights into the topology of neural networks and how neuroevolution allows you to develop complex networks, starting with simple ones. By the end of this book, you will not only have explored existing neuroevolution-based algorithms, but also have the skills you need to apply them in your research and work assignments.

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.

What you will learn

  • Discover the most popular neuroevolution algorithms – NEAT, HyperNEAT, and ES-HyperNEAT
  • Explore how to implement neuroevolution-based algorithms in Python
  • Get up to speed with advanced visualization tools to examine evolved neural network graphs
  • Understand how to examine the results of experiments and analyze algorithm performance
  • Delve into neuroevolution techniques to improve the performance of existing methods
  • Apply deep neuroevolution to develop agents for playing Atari games
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 24, 2019
Length: 368 pages
Edition : 1st
Language : English
ISBN-13 : 9781838824914
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Estimated delivery fee Deliver to Philippines

Standard delivery 10 - 13 business days

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Product Details

Publication date : Dec 24, 2019
Length: 368 pages
Edition : 1st
Language : English
ISBN-13 : 9781838824914
Category :
Languages :

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Frequently bought together


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Table of Contents

17 Chapters
Section 1: Fundamentals of Evolutionary Computation Algorithms and Neuroevolution Methods Chevron down icon Chevron up icon
Overview of Neuroevolution Methods Chevron down icon Chevron up icon
Python Libraries and Environment Setup Chevron down icon Chevron up icon
Section 2: Applying Neuroevolution Methods to Solve Classic Computer Science Problems Chevron down icon Chevron up icon
Using NEAT for XOR Solver Optimization Chevron down icon Chevron up icon
Pole-Balancing Experiments Chevron down icon Chevron up icon
Autonomous Maze Navigation Chevron down icon Chevron up icon
Novelty Search Optimization Method Chevron down icon Chevron up icon
Section 3: Advanced Neuroevolution Methods Chevron down icon Chevron up icon
Hypercube-Based NEAT for Visual Discrimination Chevron down icon Chevron up icon
ES-HyperNEAT and the Retina Problem Chevron down icon Chevron up icon
Co-Evolution and the SAFE Method Chevron down icon Chevron up icon
Deep Neuroevolution Chevron down icon Chevron up icon
Section 4: Discussion and Concluding Remarks Chevron down icon Chevron up icon
Best Practices, Tips, and Tricks Chevron down icon Chevron up icon
Concluding Remarks Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
(1 Ratings)
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4 star 0%
3 star 100%
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1 star 0%
Rohitashwa Kumar Jun 09, 2020
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Buy this book for the GitHub links to the codes only. Apart from that the books only tells about setting up virtual environment on the computer for all the codes. Codes themselves are not explained very well. What's written in the book one can figure out by just looking at the codes. That's the beauty of Python language. And what needs to be explained is not there in this book. There is a brief comparison of different implementations but that's too brief and leaves too many questions unanswered.
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