Evolutionary Deep Learning: Genetic algorithms and neural networks (Final Release)

[BaDshaH]

English | 2023 | ISBN: 1617299529 | 362 pages | True PDF | 56.91 MB

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.
Summary
In Evolutionary Deep Learning you will learn how
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.
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
Evolutionary Deep Learning introduces evolutionary computation (EC) and gives you a toolbox of techniques you can apply throughout the deep learning pipeline. Discover genetic algorithms and EC approaches to network topology, generative modeling, reinforcement learning, and more! Interactive Colab notebooks give you an opportunity to experiment as you explore.
What's inside
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 NeuroEvolution of Augmenting Topologies
11 Evolutionary learning with NEAT
12 Evolutionary machine learning and beyond



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