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Machine learning

See below for a selection of the latest books from Machine learning category. Presented with a red border are the Machine learning books that have been lovingly read and reviewed by the experts at Lovereading. With expert reading recommendations made by people with a passion for books and some unique features Lovereading will help you find great Machine learning books and those from many more genres to read that will keep you inspired and entertained. And it's all free!

Mathematics for Machine Learning

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.

Mathematics for Machine Learning

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.

Transfer Learning

Transfer Learning

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Practical Machine Learning with Rust Creating Intelligent Applications in Rust

Practical Machine Learning with Rust Creating Intelligent Applications in Rust

Author: Joydeep Bhattacharjee Format: Paperback / softback Release Date: 24/12/2019

Explore machine learning in Rust and learn about the intricacies of creating machine learning applications. This book begins by covering the important concepts of machine learning such as supervised, unsupervised, and reinforcement learning, and the basics of Rust. Further, you'll dive into the more specific fields of machine learning, such as computer vision and natural language processing, and look at the Rust libraries that help create applications for those domains. We will also look at how to deploy these applications either on site or over the cloud. After reading Practical Machine Learning with Rust, you will have a solid understanding of creating high computation libraries using Rust. Armed with the knowledge of this amazing language, you will be able to create applications that are more performant, memory safe, and less resource heavy. What You Will Learn Write machine learning algorithms in Rust Use Rust libraries for different tasks in machine learning Create concise Rust packages for your machine learning applications Implement NLP and computer vision in Rust Deploy your code in the cloud and on bare metal servers Who This Book Is For Machine learning engineers and software engineers interested in building machine learning applications in Rust.

Handbook Of Machine Learning - Volume 2: Optimization And Decision Making

Handbook Of Machine Learning - Volume 2: Optimization And Decision Making

Building on Handbook of Machine Learning - Volume 1: Foundation of Artificial Intelligence, this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.

Metareasoning Thinking about Thinking

Metareasoning Thinking about Thinking

Experts report on the latest artificial intelligence research concerning reasoning about reasoning itself. The capacity to think about our own thinking may lie at the heart of what it means to be both human and intelligent. Philosophers and cognitive scientists have investigated these matters for many years. Researchers in artificial intelligence have gone further, attempting to implement actual machines that mimic, simulate, and perhaps even replicate this capacity, called metareasoning. In this volume, leading authorities offer a variety of perspectives-drawn from philosophy, cognitive psychology, and computer science-on reasoning about the reasoning process. The book offers a simple model of reasoning about reason as a framework for its discussions. Following this framework, the contributors consider metalevel control of computational activities, introspective monitoring, distributed metareasoning, and, putting all these aspects of metareasoning together, models of the self. Taken together, the chapters offer an integrated narrative on metareasoning themes from both artificial intelligence and cognitive science perspectives.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Author: J. Joshua Thomas Format: Hardback Release Date: 30/11/2019

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Handbook of Research on Applications and Implementations of Machine Learning Techniques

Handbook of Research on Applications and Implementations of Machine Learning Techniques

Author: Sathiyamoorthi Velayutham Format: Hardback Release Date: 30/11/2019

Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world. The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Author: J. Joshua Thomas Format: Paperback / softback Release Date: 30/11/2019

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Deep Learning and Neural Networks Concepts, Methodologies, Tools, and Applications

Deep Learning and Neural Networks Concepts, Methodologies, Tools, and Applications

Author: Information Resources Management Association Format: Hardback Release Date: 30/11/2019

Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.

Fundamentals of Machine Learning

Fundamentals of Machine Learning

Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life. This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning. Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries 'sklearn' and 'Keras'. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society. Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences.

Variational Bayesian Learning Theory

Variational Bayesian Learning Theory

Variational Bayesian learning is one of the most popular methods in machine learning. Designed for researchers and graduate students in machine learning, this book summarizes recent developments in the non-asymptotic and asymptotic theory of variational Bayesian learning and suggests how this theory can be applied in practice. The authors begin by developing a basic framework with a focus on conjugacy, which enables the reader to derive tractable algorithms. Next, it summarizes non-asymptotic theory, which, although limited in application to bilinear models, precisely describes the behavior of the variational Bayesian solution and reveals its sparsity inducing mechanism. Finally, the text summarizes asymptotic theory, which reveals phase transition phenomena depending on the prior setting, thus providing suggestions on how to set hyperparameters for particular purposes. Detailed derivations allow readers to follow along without prior knowledge of the mathematical techniques specific to Bayesian learning.