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.
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
About the Author
Micheal Lanham is a proven software and tech innovator with over 20 years of experience. He has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development.
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