Evolutionary Deep Learning: Genetic algorithms and neural networks
by Michael Lanham (Author)
Publisher Finelybook 出版社：Manning (February 28, 2023)
pages 页数：350 pages
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.
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.
Google Colab notebooks make it easy to experiment and play around with each exciting example. By the time you’ve finished reading Evolutionary Deep Learning, you’ll be ready to build deep learning models as self-sufficient systems you can efficiently adapt to changing requirements.
Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
1 Introduction to Evolutionary Deep Learning
2 An Introduction to Evolutionary Computation
3 An Introduction to Genetic Algorithms with DEAP
4 More Evolutionary Computation with DEAP
5 Automating Hyperparameter Optimization
6 Neuroevolution Optimization
7 Evolutionary Convolutional Neural Networks
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
MEAP Edition Manning Early Access Program Evolutionary Deep Learning Version 10 305p