{"product_id":"bayesian-optimization-theory-and-practice-using-python-paperback","title":"Bayesian Optimization: Theory and Practice Using Python - Paperback","description":"\u003cp\u003eby \u003cb\u003ePeng Liu\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\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 this book, you will have a firm grasp of Bayesian optimization techniques, which you'll be able to put into practice in your own machine learning models.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003e\u003cb\u003eWhat You Will Learn\u003c\/b\u003e\u003cul\u003e\n\u003cli\u003eApply Bayesian Optimization to build better machine learning models\u003c\/li\u003e\n\u003cli\u003eUnderstand and research existing and new Bayesian Optimization techniques\u003c\/li\u003e\n\u003cli\u003eLeverage high-performance libraries such as BoTorch, which offer you the ability to dig into and edit the inner working\u003c\/li\u003e\n\u003cli\u003eDig into the inner workings of common optimization algorithms used to guide the search process in Bayesian optimization\u003cbr\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cb\u003eWho This Book Is For\u003c\/b\u003eBeginner to intermediate level professionals in machine learning, analytics or other roles relevant in data science.\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\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\u003eAfter completing this book, you will have a firm grasp of Bayesian optimization techniques, which you'll be able to put into practice in your own machine learning models.\u003cbr\u003e\u003c\/p\u003e\u003cbr\u003eYou will: \u003cul\u003e\n\u003cli\u003eApply Bayesian Optimization to build better machine learning models\u003c\/li\u003e\n\u003cli\u003eUnderstand and research existing and new Bayesian Optimization techniques\u003c\/li\u003e\n\u003cli\u003eLeverage high-performance libraries such as BoTorch, which offer you the ability to dig into and edit the inner working\u003c\/li\u003e\n\u003cli\u003eDig into the inner workings of common optimization algorithms used to guide the search process in Bayesian optimization\u003c\/li\u003e\n\u003c\/ul\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003cb\u003ePeng Liu\u003c\/b\u003e is an assistant professor of quantitative finance (practice) at Singapore Management University and an adjunct researcher at the National University of Singapore. He holds a Ph.D. in statistics from the National University of Singapore and has ten years of working experience as a data scientist across the banking, technology, and hospitality industries\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 234\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 0.53 x 10 x 7 IN\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003eIllustrated:\u003c\/strong\u003e Yes\u003c\/div\u003e\u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 24, 2023\u003c\/div\u003e","brand":"Books by splitShops","offers":[{"title":"Default Title","offer_id":47444067123378,"sku":"9781484290620","price":64.78,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0770\/3891\/1666\/files\/2d6675457ffc424f83d5149ceca19808.webp?v=1778698307","url":"https:\/\/box.dadyminds.org\/products\/bayesian-optimization-theory-and-practice-using-python-paperback","provider":"DADYMINDS BOX","version":"1.0","type":"link"}