Article June 23, 2023

Pattern Recognition and Machine Learning (Information Science and Statistics)

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. Publisher ‏ : ‎ Springer (August 17, 2006) Language ‏ : ‎ English Hardcover ‏ : ‎ 738 pages ISBN-10 ‏ : ‎ 0387310738 ISBN-13 ‏ : ‎ 978-0387310732 Item Weight ‏ : ‎ 4.73 pounds Dimensions ‏ : ‎ 7.7 x 1.3 x 10.2 inches

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Pattern Recognition and Machine Learning (Information Science and Statistics)
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This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

Publisher ‏ : ‎ Springer (August 17, 2006)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 738 pages
ISBN-10 ‏ : ‎ 0387310738
ISBN-13 ‏ : ‎ 978-0387310732
Item Weight ‏ : ‎ 4.73 pounds
Dimensions ‏ : ‎ 7.7 x 1.3 x 10.2 inches

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Rating Star: 4.5

Linear Algebra and Optimization for Machine Learning: A Textbook

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows:1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning. Publisher ‏ : ‎ Springer; 1st ed. 2020 edition (May 13, 2020) Language ‏ : ‎ English Hardcover ‏ : ‎ 516 pages ISBN-10 ‏ : ‎ 3030403432 ISBN-13 ‏ : ‎ 978-3030403430 Item Weight ‏ : ‎ 2.61 pounds Dimensions ‏ : ‎ 7 x 1.13 x 10 inches

ai-book Jun 24, 2023

Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges

Artificial Intelligence in Earth Science: Best Practices and Fundamental Challenges provides a comprehensive, step-by-step guide to AI workflows for solving problems in Earth Science. The book focuses on the most challenging problems in applying AI in Earth system sciences, such as training data preparation, model selection, hyperparameter tuning, model structure optimization, spatiotemporal generalization, transforming model results into products, and explaining trained models. In addition, it provides full-stack workflow tutorials to help walk readers through the whole process, regardless of previous AI experience. The book tackles the complexity of Earth system problems in AI engineering, fully guiding geoscientists who are planning to implement AI in their daily work.Provides practical, step-by-step guides for Earth Scientists who are interested in implementing AI techniques in their workFeatures case studies to show real-world examples of techniques described in the bookIncludes additional elements to help readers who are new to AI, including end-of-chapter, key concept bulleted lists that concisely cover key concepts in the chapter ASIN ‏ : ‎ B0C3VCGT3K Publisher ‏ : ‎ Elsevier (April 27, 2023) Publication date ‏ : ‎ April 27, 2023 Language ‏ : ‎ English File size ‏ : ‎ 99612 KB Text-to-Speech ‏ : ‎ Enabled Enhanced typesetting ‏ : ‎ Enabled X-Ray ‏ : ‎ Not Enabled Word Wise ‏ : ‎ Not Enabled Sticky notes ‏ : ‎ On Kindle Scribe Print length ‏ : ‎ 430 pages Page numbers source ISBN ‏ : ‎ 0323917372

ai-book Jun 22, 2023

Artificial Intelligence: 4 books in 1: AI For Beginners + AI For Business + Machine Learning For Beginners + Machine Learning And Artificial Intelligence

<h3>Do you want to discover what Artificial Intelligence is and how it will change our life, our business, our health in future? If yes, keep reading... What comes to your mind when you hear the word, artificial intelligence (AI)? Perhaps, you think of a science fiction film by the likes of Orson Scott Card or other famous authors. Maybe you're thinking of Disney's Transformers or Wall-E. The Disneyization of AI made it seem like it was a world of pure fantasy that exists only in the human imagination. However, the reality is that we are not far from using AI in everyday life. Our use of AI continues to increase over the years. As we use Amazon, streaming music devices and other AI that predict our choices, we realize that machines and technology are starting to permeate every aspect of our existence. Basically, artificial intelligence is totally changing our lives and this is the time to be ready for this technological revolution and make the most of it. This bundle contains the following books: </h3><h3>Artificial Intelligence for beginners: You will learn a modern approach to artificial intelligence, the basics of AI and how to put it into practice in your everyday life. </h3><h3>Artificial Intelligence for business: You will find helpful and concise chapters on AI topics, which can be applied to the business. The chapters draw upon real-life experiences and sources that can help you to grow your business. </h3><h3>Machine Learning for beginners: You will understand what machine learning is, how it works and how is correlated to artificial intelligence and deep learning</h3><h3></h3><h3>Machine Learning and Artificial Intelligence: In this final part you will find a broad overview of these technologies and the answers to the most frequently asked questions: will artificial intelligence change our privacy? Is artificial intelligence dangerous? Will humans and Artificial Intelligence live together in future? This 4 books in 1 bundle is all you need to discover the world of Artificial Intelligence and Machine learning!! What are you waiting for? Buy now and get your copy! </h3> ASIN ‏ : ‎ B086PVL7CB Publisher ‏ : ‎ Independently published (April 8, 2020) Language ‏ : ‎ English Paperback ‏ : ‎ 604 pages ISBN-13 ‏ : ‎ 979-8635348352 Item Weight ‏ : ‎ 2.2 pounds Dimensions ‏ : ‎ 6 x 1.37 x 9 inches

ai-book Jun 21, 2023

Mathematics for Machine Learning

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. From the Publisher <img alt="Mathematics for Machine Learning, Cambridge University Press, linear algebra" src="https://images-na.ssl-images-amazon.com/images/G/01/x-locale/common/grey-pixel.gif" class="a-spacing-base a-lazy-loaded" data-src="https://m.media-amazon.com/images/S/aplus-media/vc/dca1fdaa-a479-4d71-838d-82dfd27029ba.__CR0,0,970,600_PT0_SX970_V1___.jpg"><img alt="Mathematics for Machine Learning, Cambridge University Press, linear algebra" src="https://m.media-amazon.com/images/S/aplus-media/vc/dca1fdaa-a479-4d71-838d-82dfd27029ba.__CR0,0,970,600_PT0_SX970_V1___.jpg"> Publisher ‏ : ‎ Cambridge University Press; 1st edition (April 23, 2020) Language ‏ : ‎ English Paperback ‏ : ‎ 398 pages ISBN-10 ‏ : ‎ 110845514X ISBN-13 ‏ : ‎ 978-1108455145 Item Weight ‏ : ‎ 1.76 pounds Dimensions ‏ : ‎ 7 x 0.92 x 10 inches