{"product_id":"bayesian-optimization-9781484290620","title":"Bayesian Optimization","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eThis book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machine learning models and learn sample-efficient approaches to global optimization.\u003c\/p\u003eThe book begins by introducing different Bayesian Optimization (BO) techniques, covering both commonly used tools and advanced topics. It follows a develop from scratch method using Python, and gradually builds up to more advanced libraries such as BoTorch, an open-source project introduced by Facebook recently. Along the way, you''ll see practical implementations of this important discipline along with thorough coverage and straightforward explanations of essential theories. This book intends to bridge the gap between researchers and practitioners, providing both with a comprehensive, easy-to-digest, and useful reference guide.\u003cp\u003e\u003c\/p\u003e\u003cp\u003e    After completing \u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e●\tChapter 1: Bayesian Optimization in a Nutshell\u003cbr\u003eo\tChapter goal: introducing Bayesian Optimization  workflow and key conceptso\tEstimate number of pages: 30o\tSub topics:▪\tWhat and why of Bayesian Optimization ▪\tKey components in Bayesian Optimization  process▪\tCommon Bayesian Optimization  applications\u003cbr\u003e●\tChapter 2: Bayesian Optimization in Hyperparameter Tuningo\tChapter goal: Showcase using Bayesian Optimization  for hyperparameter tuning in training better ML modelso\tEstimate number of pages: 35o\tSub topics:▪\tML workflow▪\tCommon hyperparameter tuning techniques▪\tAdvantage of Bayesian Optimization  in tuning hyperparameters for ML models through practical examples\u003cbr\u003e●\tChapter 3 : Gaussian Processo\tChapter goal: Introduce Gaussian process and its role in Bayesian Optimization  workflowo\tEstimate number of pages: 30o\tSub topics:▪\tGaussian process  breakdown▪\tTheory illustration on Gaussian process ▪\tCoding Gaussian process  as surrogate model in Bayesian Optimization \u003cbr\u003e●\tChapter 4 : Common Acquisition Functiono\tChapter goal: Introduce popular acquisition functions including EI, PI and otherso\tEstimate number of pages: 35o\tSub topics:▪\tThe role of acquisition function in Bayesian Optimization ▪\tTheoretical basics for each common AF▪\tCoding examples\u003cbr\u003e●\tChapter 5:  Advanced Acquisition Functiono\tChapter goal: Introduce advanced acquisition functions including KG and PE and parallel variantso\tEstimate number of pages: 35o\tSub topics:▪\tTheoretical basics for advanced AF▪\tCoding examples\t●\tChapter 6 : Introducing BoTorcho\tChapter goal: Introduce the recent GPU based package for running Bayesian Optimization  o\tEstimate number of pages: 40o\tSub topics:▪\tIntroduction of the package and key components▪\tStarting examples▪\tAdvanced examples\u003cbr\u003e●\tChapter 7 : Case studyo\tChapter goal: Demonstrate full working examples using Bayesian Optimization  and BoTorcho\tEstimate number of pages: 30o\tSub topics:▪\tTwo full coding examples TBD\u003cbr\u003e●\tChapter 8 : Exotic Bayesian Optimization Problemso\tChapter goal: Introduce additional Bayesian Optimization  variants such as adding constraints and getting noisy observationso\tEstimate number of pages: 30o\tSub topics:▪\tConstrained Bayesian Optimization ▪\tParallel Bayesian Optimization ▪\tBO with noisy observations▪\tLook ahead Bayesian Optimization \u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"APress","offers":[{"title":"Default Title","offer_id":48885831401815,"sku":"9781484290620","price":999.99,"currency_code":"GBP","in_stock":false}],"url":"https:\/\/bookcurl.com\/products\/bayesian-optimization-9781484290620","provider":"Book Curl","version":"1.0","type":"link"}