Evolutionary Deep Learning Genetic algorithms and neural networks 1st Edition by Micheal Lanham ISBN 1617299529 978-1617299520

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Product details: 

ISBN 10:1617299529 

ISBN 13: 978-1617299520

Author:Micheal Lanham

Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learning’s common pitfalls and deliver adaptable model upgrades without constant manual adjustment.

In Evolutionary Deep Learning you will learn how to:

• Solve complex design and analysis problems with evolutionary computation
• Tune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization
• Use unsupervised learning with a deep learning autoencoder to regenerate sample data
• Understand the basics of reinforcement learning and the Q-Learning equation
• Apply Q-Learning to deep learning to produce deep reinforcement learning
• Optimize the loss function and network architecture of unsupervised autoencoders
• Make an evolutionary agent that can play an OpenAI Gym game

Evolutionary Deep Learning is a guide to improving your deep learning models with AutoML enhancements based on the principles of biological evolution. This exciting new approach utilizes lesser-known AI approaches to boost performance without hours of data annotation or model hyperparameter tuning. In this one-of-a-kind guide, you’ll discover tools for optimizing everything from data collection to your network architecture.

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

Table of contents: 

1.part 1 – getting started

• introducing evolutionary deep learning

• introducing evolutionary computation

• introducing genetic algorithms with deap

• more evolutionary computation with deap

2.part 2 – optimizing deep learning

• automating hyperparameter optimization

• neuroevolution optimization

• evolutionary convolutional neural networks

3.part 3 – advanced applications

• evolving autoencoders

• generative deep learning and evolution

• neat: neuroevolution of augmenting topologies

• evolutionary learning with neat

• evolutionary machine learning and beyond

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Tags: Micheal Lanham, Evolutionary, Deep, Learning