Evolutionary Deep Learning: Genetic Algorithms and Neural Networks by Micheal La

Description: Evolutionary Deep Learning by Micheal Lanham 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 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 game 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. about the technology Evolutionary deep learning merges the biology-simulating practices of evolutionary computation (EC) with the neural networks of deep learning. This unique approach can automate entire DL systems and help uncover new strategies and architectures. It gives new and aspiring AI engineers a set of optimization tools that can reliably improve output without demanding an endless churn of new data. about the reader For data scientists who know Python. FORMAT Paperback LANGUAGE English CONDITION Brand New Back Cover In Evolutionary Deep Learning youll master a toolbox of EC techniques that can be applied to any stage of the deep learning pipeline--from data collection, to hyperparameter tuning, and even optimizing network architecture. Hands-on examples demonstrate genetic algorithms and other EC approaches in action, and apply evolutionary deep learning to network topology, criterion loss and rewards, generative modeling, and reinforcement learning. Google Colab notebooks make it easy to experiment and play around with each exciting example. By the time youve finished reading, youll be ready to build deep learning models as self-sufficient systems you can efficiently adapt to changing requirements. Author Biography Micheal 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. Details ISBN1617299529 Short Title Evolutionary Deep Learning Pages 350 Language English ISBN-10 1617299529 ISBN-13 9781617299520 Format Paperback Subtitle Genetic Algorithms and Neural Networks Publisher Manning Publications Imprint Manning Publications Year 2023 Author Micheal Lanham Place of Publication New York Country of Publication United States AU Release Date 2023-10-05 NZ Release Date 2023-10-05 DEWEY 006.31 Audience Professional & Vocational UK Release Date 2023-07-06 Publication Date 2023-07-06 US Release Date 2023-07-06 We've got this At The Nile, if you're looking for it, we've got it. With fast shipping, low prices, friendly service and well over a million items - you're bound to find what you want, at a price you'll love! TheNile_Item_ID:142687296;

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