Evolutionary Deep Learning (MEAP V12)

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. Discover one-of-a-kind AI strategies never before seen outside of academic papers! Learn how the principles of evolutionary computation overcome deep learnings common pitfalls and deliver adaptable model upgrades without constant manual adjustment. 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, youll 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 Copyright_2023_Manning_Publications 4 welcome 5 1_Introduction_to_evolutionary_deep_learning 7 2_An_introduction_to_evolutionary_computation 27 3_An_introduction_to_genetic_algorithms_with_DEAP 66 4_More_evolutionary_computation_with_DEAP 103 5_Automating_hyperparameter_optimization 140 6_Neuroevolution_optimization 186 7_Evolutionary_convolutional_neural_networks 214 8_Evolving_autoencoders 249 9_Generative_deep_learning_and_evolution 288 10_NEAT:_NeuroEvolution_of_Augmenting_Topologies 326 11_Evolutionary_learning_with_NEAT 358 12_Evolutionary_machine_learning_and_beyond 391 Appendix_A_Accessing_source_code 440