Evolutionary Deep Learning (MEAP V12)

✍ Scribed by Micheal Lanham

Publisher: Manning Publications
Year: 2023
Tongue: English
Leaves: 443
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Copyright_2023_Manning_Publications
welcome
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
Appendix_A_Accessing_source_code

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