Lanham Michael. Evolutionary Deep Learning: Genetic algorithms and neural networks

Manning Publications, 2023. — 362 p. — (Final Release). — ISBN: 978-1617299520.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. In this one-of-a-kind guide, you’ll discover tools for optimizing everything from data collection to your network architecture.About the technologyDeep 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 cutting-edge Data Science.
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.About the bookEvolutionary 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 allow you to experiment as you explore.GETTING STARTED.
Introducing evolutionary deep learning.
Introducing evolutionary computation.
Introducing genetic algorithms with DEAP.
More evolutionary computation with DEAP.OPTIMIZING DEEP LEARNING.
Automating hyperparameter optimization.
Neuroevolution optimization.
Evolutionary convolutional neural networks.ADVANCED APPLICATIONS.
Evolving autoencoders.
Generative deep learning and evolution.
NeuroEvolution of Augmenting Topologies.
Evolutionary learning with NEAT.
Evolutionary machine learning and beyond.