{"product_id":"evolutionary-deep-learning-micheal-lanham","title":"Evolutionary Deep Learning by Micheal Lanham | Genetic Algorithms \u0026 Neural Networks","description":"\u003ch3\u003e\u003cspan\u003eEvolutionary Deep Learning by Micheal Lanham\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eEvolutionary Deep Learning: Genetic Algorithms and Neural Networks\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e by \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eMicheal Lanham\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e is a practical guide to combining evolutionary computation with deep learning to create more adaptable, optimized, and intelligent machine learning systems. The book explores how concepts inspired by biological evolution can be applied to neural networks, automated machine learning, model optimization, generative AI, and reinforcement learning.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eRather than relying entirely on manual experimentation and conventional optimization techniques, this book introduces evolutionary approaches that can help automate difficult machine learning decisions. Readers learn how genetic algorithms, evolutionary computation, and related optimization methods can be applied throughout the deep learning pipeline, from hyperparameter tuning and network architecture to advanced neural network applications.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eAbout This Book\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eDeep learning models can become increasingly complex as developers experiment with architectures, parameters, training methods, and datasets. Finding the best configuration can require significant time and computational resources.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eEvolutionary Deep Learning\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e explores how evolutionary computation can help address these challenges. By treating possible model configurations as candidates in an evolutionary search, developers can automate aspects of optimization and discover solutions that may be difficult to find through manual tuning.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book provides a toolbox of evolutionary computation techniques that can be applied across different stages of the deep learning process, making it especially relevant to developers interested in \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eAutoML, machine learning optimization, neural networks, and computational intelligence\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eEvolutionary Computation and Deep Learning\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eEvolutionary computation takes inspiration from biological evolution. Instead of improving a single solution directly, evolutionary algorithms can work with populations of potential solutions and use mechanisms such as selection, mutation, and crossover to search for better results.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book explains how these ideas can complement deep learning and provide alternative approaches to difficult optimization problems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eReaders can explore:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eEvolutionary computation\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eGenetic algorithms\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePopulation-based optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eFitness functions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMutation and crossover\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eEvolutionary search\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eModel optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eNeural network optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAutomated machine learning\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThis combination of evolutionary methods and deep learning forms the foundation of the book.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eGenetic Algorithms with DEAP\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book introduces genetic algorithms and demonstrates their practical use with \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eDEAP\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e, a Python framework for evolutionary computation.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eReaders can learn how genetic algorithms can be implemented programmatically and used to solve optimization problems. This hands-on approach helps bridge the gap between theoretical evolutionary concepts and practical machine learning development.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe material is particularly useful for Python developers and data scientists who want to experiment with evolutionary algorithms in real machine learning workflows.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eHyperparameter Optimization\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eSelecting appropriate hyperparameters is one of the recurring challenges in deep learning.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eEvolutionary Deep Learning\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e explores how evolutionary computation can automate hyperparameter optimization, allowing algorithms to search through possible configurations instead of requiring developers to manually test every combination.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThis can be useful for optimizing settings related to:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eNeural network training\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eModel architecture\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLearning processes\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eHyperparameters\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eValidation\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLoss functions\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eModel performance\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe approach connects evolutionary search with the broader goals of \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eautomated machine learning (AutoML)\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eNeuroevolution and Neural Network Architecture\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book explores \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eneuroevolution\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e, where evolutionary algorithms are used to develop or optimize neural networks.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThis includes approaches for searching network structures and improving architectures through evolutionary processes. Readers are introduced to \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eNeuroEvolution of Augmenting Topologies (NEAT)\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e and its role in evolutionary machine learning.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThese techniques demonstrate how AI systems can be optimized not only by changing numerical parameters but also by exploring different network structures.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eEvolutionary Convolutional Neural Networks\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eConvolutional neural networks are widely used for computer vision and other deep learning applications. Designing an effective CNN architecture can involve numerous decisions.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book examines how evolutionary methods can be applied to convolutional neural networks, helping readers understand how architecture optimization can be approached using evolutionary computation.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThis provides an interesting alternative to purely manual neural architecture design.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eAutoencoders and Generative Deep Learning\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eAdvanced sections of the book explore the application of evolutionary methods to \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eunsupervised learning, autoencoders, and generative deep learning\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eReaders learn how evolutionary optimization can be used to improve aspects of autoencoder systems, including network architecture and loss-function optimization.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book also investigates how evolutionary approaches can be combined with generative deep learning, providing opportunities to experiment with alternative ways of creating and optimizing intelligent models.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eReinforcement Learning\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eAnother important area covered is \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003ereinforcement learning\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book introduces the fundamentals of reinforcement learning and the \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eQ-Learning\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e equation before exploring how deep learning and reinforcement learning can be combined.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eReaders can also explore evolutionary agents and applications involving environments such as \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eOpenAI Gym\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e, providing practical examples of how evolutionary intelligence can be applied to learning and decision-making systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eAutomated Machine Learning\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eOne of the broader goals of evolutionary deep learning is to reduce the amount of manual intervention required when developing machine learning systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe book connects evolutionary computation with \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eAutoML\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e, demonstrating how evolutionary approaches can assist with model selection, architecture optimization, hyperparameter tuning, validation, and loss-function optimization.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThis makes the subject particularly relevant to modern machine learning workflows where automated model development and optimization are increasingly important.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eKey Topics Covered\u003c\/span\u003e\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eEvolutionary Deep Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eArtificial Intelligence\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDeep Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMachine Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eNeural Networks\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eGenetic Algorithms\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eEvolutionary Computation\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eGenetic Programming\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAutoML\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAutomated Machine Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eHyperparameter Optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eNeural Network Optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eNeuroevolution\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eNEAT\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eConvolutional Neural Networks\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAutoencoders\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eGenerative Deep Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eReinforcement Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eQ-Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDeep Reinforcement Learning\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eModel Selection\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eNetwork Architecture Optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eLoss Function Optimization\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePython\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDEAP\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eOpenAI Gym\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eComputational Intelligence\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe publisher's contents specifically include evolutionary computation, genetic algorithms with DEAP, automated hyperparameter optimization, neuroevolution, evolutionary CNNs, evolving autoencoders, generative deep learning, NEAT, and evolutionary machine learning.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eWhy Read This Book?\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eEvolutionary Deep Learning\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e is an excellent choice for readers who already have some knowledge of Python and machine learning and want to explore alternative approaches to deep learning optimization.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eInstead of focusing exclusively on traditional gradient-based optimization, the book introduces biology-inspired computational techniques that can search through complex solution spaces and automate aspects of model development.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eIt is particularly interesting for readers exploring the intersection of \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003edeep learning, evolutionary algorithms, AutoML, neural architecture optimization, and reinforcement learning\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eWho Should Read This?\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThis book is suitable for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003eData scientists\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMachine learning engineers\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAI developers\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003ePython programmers\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDeep learning practitioners\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eComputer science students\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eArtificial intelligence students\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eMachine learning researchers\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eAI enthusiasts\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelopers interested in AutoML\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eDevelopers exploring genetic algorithms\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eProfessionals working with neural networks\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003eReaders interested in evolutionary computation\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eThe publisher specifically describes the intended reader as \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003edata scientists who know Python\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e and positions the book at an intermediate-to-advanced level.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eProduct Details\u003c\/span\u003e\u003c\/h3\u003e\n\u003cul\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eBook Title:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Evolutionary Deep Learning: Genetic Algorithms and Neural Networks\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eAuthor:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Micheal Lanham\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003ePublisher:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Manning Publications\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003ePublication Date:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e 2023\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eLanguage:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e English\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eGenre:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Artificial Intelligence \/ Machine Learning \/ Deep Learning\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eISBN-13:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e 9781617299520\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003ePages:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e 360\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eFormat:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e English Paperback \/ Trade Paperback\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003eLevel:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Intermediate to Advanced\u003c\/span\u003e\n\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e\u003cspan\u003ePrimary Topics:\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e Evolutionary Computation, Genetic Algorithms, Neural Networks, Deep Learning, AutoML\u003c\/span\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eManning and the official publisher listing confirm the 2023 publication, ISBN 9781617299520, and 360-page length.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eAbout the Author\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eMicheal Lanham\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e is a software and technology innovator with more than two decades of industry experience. His background includes software development across games, graphics, web, desktop engineering, artificial intelligence, GIS, and machine learning. He began working with neural networks and evolutionary algorithms in game development around the turn of the millennium.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cspan\u003eLanham has also authored \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003eAI Agents in Action\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e, continuing his focus on practical applications of modern artificial intelligence and machine learning.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch3\u003e\u003cspan\u003eFinal Overview\u003c\/span\u003e\u003c\/h3\u003e\n\u003cp class=\"isSelectedEnd\"\u003e\u003cstrong\u003e\u003cspan\u003eEvolutionary Deep Learning by Micheal Lanham\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e provides a practical exploration of how evolutionary computation can enhance modern deep learning. By combining \u003c\/span\u003e\u003cstrong\u003e\u003cspan\u003egenetic algorithms, neural networks, AutoML, neuroevolution, hyperparameter optimization, evolutionary CNNs, autoencoders, generative learning, NEAT, and reinforcement learning\u003c\/span\u003e\u003c\/strong\u003e\u003cspan\u003e, the book gives readers a broader perspective on building and optimizing intelligent systems.\u003c\/span\u003e\u003c\/p\u003e\n\u003ch4\u003e\u003cspan\u003eFor Python-based data scientists and machine learning practitioners looking to explore innovative approaches to AI optimization, this book offers a valuable introduction to the rapidly evolving field of evolutionary deep learning.\u003c\/span\u003e\u003c\/h4\u003e","brand":"BookBeen","offers":[{"title":"Default Title","offer_id":53462521643317,"sku":null,"price":1100.0,"currency_code":"PKR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0972\/1731\/5125\/files\/BookBeen-2026-09-04T212632.217.png?v=1788540189","url":"https:\/\/bookbeen.com\/products\/evolutionary-deep-learning-micheal-lanham","provider":"Bookbeen","version":"1.0","type":"link"}