{"product_id":"bayesian-optimization-in-action-9781633439078","title":"Bayesian Optimization in Action","description":"\u003cb\u003eBayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy. Put its advanced techniques into practice with this hands-on guide.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eIn \u003ci\u003eBayesian Optimization in Action\u003c\/i\u003e you will learn how to: \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eTrain Gaussian processes on both sparse and large data sets\u003c\/li\u003e \u003cli\u003eCombine Gaussian processes with deep neural networks to make them flexible and expressive\u003c\/li\u003e \u003cli\u003eFind the most successful strategies for hyperparameter tuning\u003c\/li\u003e \u003cli\u003eNavigate a search space and identify high-performing regions\u003c\/li\u003e \u003cli\u003eApply Bayesian optimization to cost-constrained, multi-objective, and preference optimization\u003c\/li\u003e \u003cli\u003eImplement Bayesian optimization with PyTorch, GPyTorch, and BoTorch\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e\u003ci\u003eBayesian Optimization in Action\u003c\/i\u003e shows you how to optimize hyperparameter tuning, A\/B testing, and other aspects of the machine learning process by applying cutting-edge Bayesian techniques. Using clear language, illustrations, and concrete examples, this book proves that Bayesian optimization doesn't have to be difficult! You'll get in-depth insights into how Bayesian optimization works and learn how to implement it with cutting-edge Python libraries. The book's easy-to-reuse code samples let you hit the ground running by plugging them straight into your own projects. \u003cp\u003e\u003c\/p\u003e Forewords by Luis Serrano and David Sweet\u003ci\u003e.\u003c\/i\u003e \u003cp\u003e\u003c\/p\u003e Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e In machine learning, optimization is about achieving the best predictions--shortest delivery routes, perfect price points, most accurate recommendations--in the fewest number of steps. Bayesian optimization uses the mathematics of probability to fine-tune ML functions, algorithms, and hyperparameters efficiently when traditional methods are too slow or expensive. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eBayesian Optimization in Action\u003c\/i\u003e teaches you how to create efficient machine learning processes using a Bayesian approach. In it, you'll explore practical techniques for training large datasets, hyperparameter tuning, and navigating complex search spaces. This interesting book includes engaging illustrations and fun examples like perfecting coffee sweetness, predicting weather, and even debunking psychic claims. You'll learn how to navigate multi-objective scenarios, account for decision costs, and tackle pairwise comparisons. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eGaussian processes for sparse and large datasets\u003c\/li\u003e \u003cli\u003eStrategies for hyperparameter tuning\u003c\/li\u003e \u003cli\u003eIdentify high-performing regions\u003c\/li\u003e \u003cli\u003eExamples in PyTorch, GPyTorch, and BoTorch\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e For machine learning practitioners who are confident in math and statistics. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e \u003cb\u003eQuan Nguyen\u003c\/b\u003e is a research assistant at Washington University in St. Louis. He writes for the Python Software Foundation and has authored several books on Python programming. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 1 Introduction to Bayesian optimization\u003cbr\u003e PART 1 MODELING WITH GAUSSIAN PROCESSES\u003cbr\u003e 2 Gaussian processes as distributions over functions\u003cbr\u003e 3 Customizing a Gaussian process with the mean and covariance functions\u003cbr\u003e PART 2 MAKING DECISIONS WITH BAYESIAN OPTIMIZATION\u003cbr\u003e 4 Refining the best result with improvement-based policies\u003cbr\u003e 5 Exploring the search space with bandit-style policies\u003cbr\u003e 6 Leveraging information theory with entropy-based policies\u003cbr\u003e PART 3 EXTENDING BAYESIAN OPTIMIZATION TO SPECIALIZED SETTINGS\u003cbr\u003e 7 Maximizing throughput with batch optimization\u003cbr\u003e 8 Satisfying extra constraints with constrained optimization\u003cbr\u003e 9 Balancing utility and cost with multifidelity optimization\u003cbr\u003e 10 Learning from pairwise comparisons with preference optimization\u003cbr\u003e 11 Optimizing multiple objectives at the same time\u003cbr\u003e PART 4 SPECIAL GAUSSIAN PROCESS MODELS\u003cbr\u003e 12 Scaling Gaussian processes to large datasets\u003cbr\u003e 13 Combining Gaussian processes with neural networks\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cstrong\u003eQuan Nguyen\u003c\/strong\u003e is a Python programmer and machine learning enthusiast. He is interested in solving decision-making problems that involve uncertainty. Quan has authored several books on Python programming and scientific computing. He is currently pursuing a PhD degree in Computer Science at Washington University in St. Louis, where he conducts research on Bayesian methods in machine learning.\u003c\/p\u003e\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":50562490827026,"sku":"9781633439078","price":55.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_cb07c24b-69bd-414f-a5ae-9d1288214062.jpg?v=1731860586","url":"https:\/\/surprise-castle.myshopify.com\/products\/bayesian-optimization-in-action-9781633439078","provider":"Surprise Castle","version":"1.0","type":"link"}