Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms
bIncrease the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution/b h4Key Features/h4 ulliImplement neuroevolution algorithms to improve the performance of neural network architectures /li liUnderstand evolutionary al...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2019
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| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bIncrease the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution/b h4Key Features/h4 ulliImplement neuroevolution algorithms to improve the performance of neural network architectures /li liUnderstand evolutionary algorithms and neuroevolution methods with real-world examples /li liLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulations/li/ul h4Book Description/h4 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. h4What you will learn/h4 ulliDiscover the most popular neuroevolution algorithms - NEAT, HyperNEAT, and ES-HyperNEAT /li liExplore how to implement neuroevolution-based algorithms in Python /li liGet up to speed with advanced visualization tools to examine evolved neural network graphs /li liUnderstand how to examine the results of experiments and analyze algorithm performance /li liDelve into neuroevolution techniques to improve the performance of existing methods /li liApply deep neuroevolution to develop agents for playing Atari games/li/ul h4Who this book is for/h4 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 |
| Beschreibung: | 1 Online-Ressource (368 Seiten) |
| ISBN: | 9781838822002 |
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| 520 | |a bIncrease the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution/b h4Key Features/h4 ulliImplement neuroevolution algorithms to improve the performance of neural network architectures /li liUnderstand evolutionary algorithms and neuroevolution methods with real-world examples /li liLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulations/li/ul h4Book Description/h4 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. | ||
| 520 | |a 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. | ||
| 520 | |a h4What you will learn/h4 ulliDiscover the most popular neuroevolution algorithms - NEAT, HyperNEAT, and ES-HyperNEAT /li liExplore how to implement neuroevolution-based algorithms in Python /li liGet up to speed with advanced visualization tools to examine evolved neural network graphs /li liUnderstand how to examine the results of experiments and analyze algorithm performance /li liDelve into neuroevolution techniques to improve the performance of existing methods /li liApply deep neuroevolution to develop agents for playing Atari games/li/ul h4Who this book is for/h4 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 | ||
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| spelling | Omelianenko, Iaroslav Verfasser aut Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms Omelianenko, Iaroslav 1 Birmingham Packt Publishing Limited 2019 1 Online-Ressource (368 Seiten) txt rdacontent c rdamedia cr rdacarrier bIncrease the performance of various neural network architectures using NEAT, HyperNEAT, ES-HyperNEAT, Novelty Search, SAFE, and deep neuroevolution/b h4Key Features/h4 ulliImplement neuroevolution algorithms to improve the performance of neural network architectures /li liUnderstand evolutionary algorithms and neuroevolution methods with real-world examples /li liLearn essential neuroevolution concepts and how they are used in domains including games, robotics, and simulations/li/ul h4Book Description/h4 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. h4What you will learn/h4 ulliDiscover the most popular neuroevolution algorithms - NEAT, HyperNEAT, and ES-HyperNEAT /li liExplore how to implement neuroevolution-based algorithms in Python /li liGet up to speed with advanced visualization tools to examine evolved neural network graphs /li liUnderstand how to examine the results of experiments and analyze algorithm performance /li liDelve into neuroevolution techniques to improve the performance of existing methods /li liApply deep neuroevolution to develop agents for playing Atari games/li/ul h4Who this book is for/h4 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 COMPUTERS / Neural Networks COMPUTERS / Machine Theory |
| spellingShingle | Omelianenko, Iaroslav Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms COMPUTERS / Neural Networks COMPUTERS / Machine Theory |
| title | Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms |
| title_auth | Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms |
| title_exact_search | Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms |
| title_exact_search_txtP | Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms |
| title_full | Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms Omelianenko, Iaroslav |
| title_fullStr | Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms Omelianenko, Iaroslav |
| title_full_unstemmed | Hands-On Neuroevolution with Python Build high-performing artificial neural network architectures using neuroevolution-based algorithms Omelianenko, Iaroslav |
| title_short | Hands-On Neuroevolution with Python |
| title_sort | hands on neuroevolution with python build high performing artificial neural network architectures using neuroevolution based algorithms |
| title_sub | Build high-performing artificial neural network architectures using neuroevolution-based algorithms |
| topic | COMPUTERS / Neural Networks COMPUTERS / Machine Theory |
| topic_facet | COMPUTERS / Neural Networks COMPUTERS / Machine Theory |
| work_keys_str_mv | AT omelianenkoiaroslav handsonneuroevolutionwithpythonbuildhighperformingartificialneuralnetworkarchitecturesusingneuroevolutionbasedalgorithms |