{"product_id":"9781617299520","title":"Evolutionary Deep Learning: Genetic algorithms and neural networks","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eLanham, Micheal\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e7\/18\/2023\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003eDiscover 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.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eEvolutionary Deep Learning\u003c\/i\u003e you will learn how to:\u003cul\u003e \u003cli\u003eSolve complex design and analysis problems with evolutionary computation\u003c\/li\u003e \u003cli\u003eTune deep learning hyperparameters with evolutionary computation (EC), genetic algorithms, and particle swarm optimization\u003c\/li\u003e \u003cli\u003eUse unsupervised learning with a deep learning autoencoder to regenerate sample data\u003c\/li\u003e \u003cli\u003eUnderstand the basics of reinforcement learning and the Q-Learning equation\u003c\/li\u003e \u003cli\u003eApply Q-Learning to deep learning to produce deep reinforcement learning\u003c\/li\u003e \u003cli\u003eOptimize the loss function and network architecture of unsupervised autoencoders\u003c\/li\u003e \u003cli\u003eMake an evolutionary agent that can play an OpenAI Gym game\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003ci\u003eEvolutionary Deep Learning\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eDeep 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eEvolutionary Deep Learning\u003c\/i\u003e 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.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cul\u003e \u003cli\u003eSolve complex design and analysis problems with evolutionary computation\u003c\/li\u003e \u003cli\u003eTune deep learning hyperparameters\u003c\/li\u003e \u003cli\u003eApply Q-Learning to deep learning to produce deep reinforcement learning\u003c\/li\u003e \u003cli\u003eOptimize the loss function and network architecture of unsupervised autoencoders\u003c\/li\u003e \u003cli\u003eMake an evolutionary agent that can play an OpenAI Gym game\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003eFor data scientists who know Python.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eMicheal Lanham\u003c\/b\u003e is a proven software and tech innovator with over 20 years of experience.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePART 1 - GETTING STARTED\u003cbr\u003e1 Introducing \u003ci\u003eevolutionary deep learning\u003c\/i\u003e\u003cbr\u003e2 Introducing evolutionary computation\u003cbr\u003e3 Introducing genetic algorithms with DEAP\u003cbr\u003e4 More evolutionary computation with DEAP\u003cbr\u003ePART 2 - OPTIMIZING DEEP LEARNING\u003cbr\u003e5 Automating hyperparameter optimization\u003cbr\u003e6 Neuroevolution optimization\u003cbr\u003e7 Evolutionary convolutional neural networks\u003cbr\u003ePART 3 - ADVANCED APPLICATIONS\u003cbr\u003e8 Evolving autoencoders\u003cbr\u003e9 Generative deep learning and evolution\u003cbr\u003e10 NEAT: NeuroEvolution of Augmenting Topologies\u003cbr\u003e11 Evolutionary learning with NEAT\u003cbr\u003e12 Evolutionary machine learning and beyond","brand":"Manning","offers":[{"title":"Default Title","offer_id":48176701767935,"sku":"9781617299520","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_623ac484-9eb3-4413-989f-ee6368bf8e7d.jpg?v=1771351769","url":"https:\/\/massivebookshop.com\/products\/9781617299520","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}