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.

About the technology

Deep learning meets evolutionary biology in this incredible book. Explore how biology-inspired algorithms and intuitions amplify the power of neural networks to solve tricky search, optimization, and control problems. Relevant, practical, and extremely interesting examples demonstrate how ancient lessons from the natural world are shaping the cutting edge of data science.

About the book

What's inside

Solve complex design and analysis problems with evolutionary computation

Tune deep learning hyperparameters

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

About the reader

For data scientists who know Python.

About the author

Micheal Lanham

is a proven software and tech innovator with over 20 years of experience.

Table of Contents

PART 1 - GETTING STARTED

1 Introducing evolutionary deep learning

2 Introducing evolutionary computation

3 Introducing genetic algorithms with DEAP

4 More evolutionary computation with DEAP

PART 2 - OPTIMIZING DEEP LEARNING

5 Automating hyperparameter optimization

6 Neuroevolution optimization

7 Evolutionary convolutional neural networks

PART 3 - ADVANCED APPLICATIONS

8 Evolving autoencoders

9 Generative deep learning and evolution

10 NEAT: NeuroEvolution of Augmenting Topologies

11 Evolutionary learning with NEAT

12 Evolutionary machine learning and beyond

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