Author :
Publisher : Elsevier
Page : 590 pages
File Size : 48,88 MB
Release : 2024-06-13
Category : Mathematics
ISBN : 0443239851
Numerical Analysis Meets Machine Learning series, highlights new advances in the field, with this new volume presenting interesting chapters. Each chapter is written by an international board of authors. - Provides the authority and expertise of leading contributors from an international board of authors - Presents the latest release in the Handbook of Numerical Analysis series - Updated release includes the latest information on the Numerical Analysis Meets Machine Learning
Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
Page : 392 pages
File Size : 20,61 MB
Release : 2020-04-23
Category : Computers
ISBN : 1108569323
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Author : Justin Solomon
Publisher : CRC Press
Page : 400 pages
File Size : 47,60 MB
Release : 2015-06-24
Category : Computers
ISBN : 1482251892
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig
Author : Kristof T. Schütt
Publisher : Springer Nature
Page : 473 pages
File Size : 40,5 MB
Release : 2020-06-03
Category : Science
ISBN : 3030402452
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Author : Giacomo Albi
Publisher : Springer Nature
Page : 241 pages
File Size : 16,76 MB
Release : 2023-06-02
Category : Mathematics
ISBN : 3031298756
A broad range of phenomena in science and technology can be described by non-linear partial differential equations characterized by systems of conservation laws with source terms. Well known examples are hyperbolic systems with source terms, kinetic equations, and convection-reaction-diffusion equations. This book collects research advances in numerical methods for hyperbolic balance laws and kinetic equations together with related modelling aspects. All the contributions are based on the talks of the speakers of the Young Researchers’ Conference “Numerical Aspects of Hyperbolic Balance Laws and Related Problems”, hosted at the University of Verona, Italy, in December 2021.
Author : Dirk P. Kroese
Publisher : CRC Press
Page : 538 pages
File Size : 27,5 MB
Release : 2019-11-20
Category : Business & Economics
ISBN : 1000730778
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
Page : 415 pages
File Size : 49,78 MB
Release : 2014-05-19
Category : Computers
ISBN : 1107057132
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author : Jeff Hawkins
Publisher : Basic Books
Page : 251 pages
File Size : 29,38 MB
Release : 2021-03-02
Category : Computers
ISBN : 1541675800
A bestselling author, neuroscientist, and computer engineer unveils a theory of intelligence that will revolutionize our understanding of the brain and the future of AI. For all of neuroscience's advances, we've made little progress on its biggest question: How do simple cells in the brain create intelligence? Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world—not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought. A Thousand Brains heralds a revolution in the understanding of intelligence. It is a big-think book, in every sense of the word. One of the Financial Times' Best Books of 2021 One of Bill Gates' Five Favorite Books of 2021
Author : Craig A. Hill
Publisher : John Wiley & Sons
Page : 784 pages
File Size : 36,32 MB
Release : 2020-09-29
Category : Social Science
ISBN : 1118976320
Offers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.
Author : Amy Neustein
Publisher : Springer Science & Business Media
Page : 317 pages
File Size : 45,37 MB
Release : 2013-06-22
Category : Technology & Engineering
ISBN : 1461469341
Editors Amy Neustein and Judith A. Markowitz have recruited a talented group of contributors to introduce the next generation of natural language technologies to resolve some of the most vexing natural-language problems that compromise the performance of speech systems today. This fourteen-chapter anthology consists of contributions from industry scientists and from academicians working at major universities in North America and Europe. They include researchers who have played a central role in DARPA-funded programs and developers who craft real-world solutions for corporations. This anthology is aimed at speech engineers, system developers, computer scientists, AI researchers, and others interested in utilizing natural-language technology in both spoken and text-based applications.
