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.In Evolutionary Deep Learning you will learn how to:Solve complex design and analysis problems with evolutionary computationTune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimizationUse unsupervised learning with a deep learning autoencoder to regenerate sample dataUnderstand the basics of reinforcement learning and the Q-Learning equationApply Q-Learning to deep learning to produce deep reinforcement learningOptimize the loss function and network architecture of unsupervised autoencodersMake an evolutionary agent that can play an OpenAI Gym gameEvolutionary 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 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 the cutting edge of data science.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 give you an opportunity to experiment as you explore.What's insideSolve complex design and analysis problems with evolutionary computationTune deep learning hyperparametersApply Q-Learning to deep learning to produce deep reinforcement learningOptimize the loss function and network architecture of unsupervised autoencodersMake an evolutionary agent that can play an OpenAI Gym gameAbout the readerFor data scientists who know Python.About the authorMicheal Lanham is a proven software and tech innovator with over 20 years of experience. He has developed a broad range of software applications in areas such as games, graphics, web, desktop, engineering, artificial intelligence, GIS, and machine learning applications for a variety of industries. At the turn of the millennium, Micheal began working with neural networks and evolutionary algorithms in game development. Table of ContentsPART 1 - GETTING STARTED1 Introducing evolutionary deep learning2 Introducing evolutionary computation3 Introducing genetic algorithms with DEAP4 More evolutionary computation with DEAPPART 2 - OPTIMIZING DEEP LEARNING5 Automating hyperparameter optimization6 Neuroevolution optimization7 Evolutionary convolutional neural networksPART 3 - ADVANCED APPLICATIONS8 Evolving autoencoders9 Generative deep learning and evolution10 NEAT: NeuroEvolution of Augmenting Topologies11 Evolutionary learning with NEAT12 Evolutionary machine learning and beyond