Bayesian Optimization In Action
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Bayesian Optimization In Action
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Author : Quan Nguyen
language : en
Publisher: Simon and Schuster
Release Date : 2023-11-14
Bayesian Optimization In Action written by Quan Nguyen and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-14 with Computers categories.
Bayesian Optimization in Action teaches you how to build Bayesian Optimisation systems from the ground up. This book transforms state-of-the-art research into usable techniques you can easily put into practice. With a range of illustrations, and concrete examples, this book proves that Bayesian Optimisation doesn't have to be difficult!
Bayesian Optimization In Action
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Author : Quan Nguyen
language : en
Publisher: Simon and Schuster
Release Date : 2024-01-09
Bayesian Optimization In Action written by Quan Nguyen and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-09 with Computers categories.
Bayesian 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. In Bayesian Optimization in Action you will learn how to: Train Gaussian processes on both sparse and large data sets Combine Gaussian processes with deep neural networks to make them flexible and expressive Find the most successful strategies for hyperparameter tuning Navigate a search space and identify high-performing regions Apply Bayesian optimization to cost-constrained, multi-objective, and preference optimization Implement Bayesian optimization with PyTorch, GPyTorch, and BoTorch Bayesian Optimization in Action 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. Forewords by Luis Serrano and David Sweet. About the technology 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. About the book Bayesian Optimization in Action 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. What's inside Gaussian processes for sparse and large datasets Strategies for hyperparameter tuning Identify high-performing regions Examples in PyTorch, GPyTorch, and BoTorch About the reader For machine learning practitioners who are confident in math and statistics. About the author Quan Nguyen 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. Table of Contents 1 Introduction to Bayesian optimization 2 Gaussian processes as distributions over functions 3 Customizing a Gaussian process with the mean and covariance functions 4 Refining the best result with improvement-based policies 5 Exploring the search space with bandit-style policies 6 Leveraging information theory with entropy-based policies 7 Maximizing throughput with batch optimization 8 Satisfying extra constraints with constrained optimization 9 Balancing utility and cost with multifidelity optimization 10 Learning from pairwise comparisons with preference optimization 11 Optimizing multiple objectives at the same time 12 Scaling Gaussian processes to large datasets 13 Combining Gaussian processes with neural networks
Bayesian Optimization
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Author : Roman Garnett
language : en
Publisher: Cambridge University Press
Release Date : 2023-02-09
Bayesian Optimization written by Roman Garnett and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-09 with Computers categories.
Bayesian optimization is a methodology for optimizing expensive objective functions that has proven success in the sciences, engineering, and beyond. This timely text provides a self-contained and comprehensive introduction to the subject, starting from scratch and carefully developing all the key ideas along the way. This bottom-up approach illuminates unifying themes in the design of Bayesian optimization algorithms and builds a solid theoretical foundation for approaching novel situations. The core of the book is divided into three main parts, covering theoretical and practical aspects of Gaussian process modeling, the Bayesian approach to sequential decision making, and the realization and computation of practical and effective optimization policies. Following this foundational material, the book provides an overview of theoretical convergence results, a survey of notable extensions, a comprehensive history of Bayesian optimization, and an extensive annotated bibliography of applications.
Bayesian Optimization And Data Science
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Author : Francesco Archetti
language : en
Publisher: Springer Nature
Release Date : 2019-09-25
Bayesian Optimization And Data Science written by Francesco Archetti and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-25 with Business & Economics categories.
This volume brings together the main results in the field of Bayesian Optimization (BO), focusing on the last ten years and showing how, on the basic framework, new methods have been specialized to solve emerging problems from machine learning, artificial intelligence, and system optimization. It also analyzes the software resources available for BO and a few selected application areas. Some areas for which new results are shown include constrained optimization, safe optimization, and applied mathematics, specifically BO's use in solving difficult nonlinear mixed integer problems. The book will help bring readers to a full understanding of the basic Bayesian Optimization framework and gain an appreciation of its potential for emerging application areas. It will be of particular interest to the data science, computer science, optimization, and engineering communities.
Designing Deep Learning Systems
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Author : Chi Wang
language : en
Publisher: Simon and Schuster
Release Date : 2023-07-25
Designing Deep Learning Systems written by Chi Wang and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-25 with Computers categories.
To be practically usable, a deep learning model must be built into a software platform. As a software engineer, you need a deep understanding of deep learning to create such a system. This book gives you that depth. Designing deep learning systems: a guide for software engineers teaches you everything you need to design and implement a production-ready deep learning platform. First, it presents the big picture of a deep learning system from the developer's perspective, including its majot components and how they are connected. Then, it carefully guides you through the engineering methods you'll need to build your own maintainable, efficient, and scalable deep learning platforms.
Machine Learning In Farm Animal Behavior Using Python
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Author : Natasa Kleanthous
language : en
Publisher: CRC Press
Release Date : 2025-03-07
Machine Learning In Farm Animal Behavior Using Python written by Natasa Kleanthous and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-07 with Science categories.
This book is a comprehensive guide to applying machine learning to animal behavior analysis, focusing on activity recognition in farm animals. It begins by introducing key concepts of animal behavior and ethology, followed by an exploration of machine learning techniques, including supervised, unsupervised, semi-supervised, and reinforcement learning. The practical section covers essential steps like data collection, preprocessing, exploratory data analysis, feature extraction, model training, and evaluation, using Python. The book emphasizes the importance of high-quality data and discusses various sensors and annotation methods for effective data collection. It addresses key machine learning challenges such as generalization and data issues. Advanced topics include feature selection, model selection, hyperparameter tuning, and deep learning algorithms. Practical examples and Python implementations are provided throughout, offering hands-on experience for researchers, students, and professionals aiming to apply machine learning to animal behavior analysis.
Algorithmic Foundations Of Robotics Xv
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Author : Steven M. LaValle
language : en
Publisher: Springer Nature
Release Date : 2022-12-14
Algorithmic Foundations Of Robotics Xv written by Steven M. LaValle and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-14 with Technology & Engineering categories.
This book includes significant recent research on robotic algorithms. It has been written by leading experts in the field. The 15th Workshop on the Algorithmic Foundations of Robotics (WAFR) was held on June 22–24, 2022, at the University of Maryland, College Park, Maryland. Each chapter represents an exciting state-of-the-art development in robotic algorithms that was presented at this 15th incarnation of WAFR. Different chapters combine ideas from a wide variety of fields, spanning and combining planning (for tasks, paths, motion, navigation, coverage, and patrol), computational geometry and topology, control theory, machine learning, formal methods, game theory, information theory, and theoretical computer science. Many of these papers explore new and interesting problems and problem variants that include human–robot interaction, planning and reasoning under uncertainty, dynamic environments, distributed decision making, multi-agent coordination, and heterogeneity.
Robotics Research
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Author : Aude Billard
language : en
Publisher: Springer Nature
Release Date : 2023-03-07
Robotics Research written by Aude Billard and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-07 with Technology & Engineering categories.
The proceedings of the 2022 edition of the International Symposium of Robotics Research (ISRR) offer a series of peer-reviewed chapters that report on the most recent research results in robotics, in a variety of domains of robotics including robot design, control, robot vision, robot learning, planning, and integrated robot systems. The proceedings entail also invited contributions that offer provocative new ideas, open-ended themes, and new directions for robotics, written by some of the most renown international researchers in robotics. As one of the pioneering symposia in robotics, ISRR has established some of the most fundamental and lasting contributions in the field since 1983. ISRR promotes the development and dissemination of ground-breaking research and technological innovation in robotics useful to society by providing a lively, intimate, forward-looking forum for discussion and debate about the status and future trends of robotics, with emphasis on its potential role to benefit humans.
Model Validation And Uncertainty Quantification Volume 3
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Author : Roland Platz
language : en
Publisher: Springer Nature
Release Date : 2023-10-06
Model Validation And Uncertainty Quantification Volume 3 written by Roland Platz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-06 with Technology & Engineering categories.
Model Validation and Uncertainty Quantification, Volume 3: Proceedings of the 41st IMAC, A Conference and Exposition on Structural Dynamics, 2023, the third volume of ten from the Conference brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Model Validation and Uncertainty Quantification, including papers on: Introduction of Uncertainty Quantification Uncertainty Quantification in Dynamics Model Form Uncertainty and Selection incl. Round Robin Challenge Sensor and Information Fusion Virtual Sensing, Certification, and Real-Time Monitoring Surrogate Modeling
Intelligent Warfare
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Author : Mingxi Wu
language : en
Publisher: Taylor & Francis
Release Date : 2022-07-29
Intelligent Warfare written by Mingxi Wu and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-29 with Technology & Engineering categories.
This book examines the future trend toward "intelligent" warfare considering the global environment, the history of warfare, and scientific and technological advancement. It develops a comprehensive set of theoretical frameworks, application concepts, and evaluation criteria for military intelligence. The volume is packed with theoretical highlights and vivid examples, including the tracking of Osama bin Laden, the decapitation strike against Qasem Soleimani, the remote assassination of Iranian nuclear scientists, the drone war in the Nagorno–Karabakh conflict, modern equipment deployed in the Palestinian–Israeli conflict, and the war between social media groups. In addition, the author envisions a possible future for "intelligent" wars in which adversarial parties engage in combat through virtual and unmanned systems. This nature may help avoid the brutality and high death toll associated with traditional warfare. The book explores the possibility of future civilized warfare. It will be of interest to researchers, academics, and students in the fields of politics, military intelligence, and military technology, and to those who are interested in intelligent warfare in general.