December 3, 2020

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Deep Learning

Deep Learning
Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Release Date : 2016-11-18
Category : Computers
Total pages :775
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

Deep Learning
Author : Ian Goodfellow,Yoshua Bengio,Aaron Courville
Publisher : MIT Press
Release Date : 2016-11-10
Category : Computers
Total pages :800
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An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Foundations of Machine Learning

Foundations of Machine Learning
Author : Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar
Publisher : MIT Press
Release Date : 2012-08-17
Category : Computers
Total pages :414
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Fundamental topics in machine learning are presented along with theoretical and conceptual tools for the discussion and proof of algorithms.

C4.5

C4.5
Author : J. Ross Quinlan
Publisher : Morgan Kaufmann
Release Date : 1993
Category : Computers
Total pages :302
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This book is a complete guide to the C4.5 system as implemented in C for the UNIX environment. It contains a comprehensive guide to the system's use, the source code (about 8,800 lines), and implementation notes.

Understanding Machine Learning

Understanding Machine Learning
Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Release Date : 2014-05-19
Category : Computers
Total pages :409
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Optimization for Machine Learning

Optimization for Machine Learning
Author : Suvrit Sra,Sebastian Nowozin,Stephen J. Wright
Publisher : MIT Press
Release Date : 2011-09-30
Category : Computers
Total pages :512
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An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Machine Learning for Hackers

Machine Learning for Hackers
Author : Drew Conway,John White
Publisher : "O'Reilly Media, Inc."
Release Date : 2012-02-15
Category : Computers
Total pages :303
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Presents algorithms that enable computers to train themselves to automate tasks, focusing on specific problems such as prediction, optimization, and classification.

Machine Learning

Machine Learning
Author : Kai-Zhu Huang,Haiqin Yang,Irwin King,Michael R. Lyu
Publisher : Springer Science & Business Media
Release Date : 2008-09-24
Category : Computers
Total pages :169
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Machine Learning - Modeling Data Locally and Globally presents a novel and unified theory that tries to seamlessly integrate different algorithms. Specifically, the book distinguishes the inner nature of machine learning algorithms as either "local learning"or "global learning."This theory not only connects previous machine learning methods, or serves as roadmap in various models, but – more importantly – it also motivates a theory that can learn from data both locally and globally. This would help the researchers gain a deeper insight and comprehensive understanding of the techniques in this field. The book reviews current topics,new theories and applications. Kaizhu Huang was a researcher at the Fujitsu Research and Development Center and is currently a research fellow in the Chinese University of Hong Kong. Haiqin Yang leads the image processing group at HiSilicon Technologies. Irwin King and Michael R. Lyu are professors at the Computer Science and Engineering department of the Chinese University of Hong Kong.

Machine Learning

Machine Learning
Author : Stephen Marsland
Publisher : CRC Press
Release Date : 2011-03-23
Category : Business & Economics
Total pages :406
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Traditional books on machine learning can be divided into two groups- those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but

Introduction to Machine Learning

Introduction to Machine Learning
Author : Ethem Alpaydin
Publisher : MIT Press
Release Date : 2004
Category : Computers
Total pages :415
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An introductory text in machine learning that gives a unified treatment of methods based on statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.

Machine Learning

Machine Learning
Author : Tony Jebara
Publisher : Springer Science & Business Media
Release Date : 2003-12-31
Category : Computers
Total pages :200
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Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two schools of thought together within a common framework, elegantly connecting their various theories and making one common big-picture. Also, this bridge brings forth new hybrid discriminative-generative tools that combine the strengths of both camps. This book serves multiple purposes as well. The framework acts as a scientific breakthrough, fusing the areas of generative and discriminative learning and will be of interest to many researchers. However, as a conceptual breakthrough, this common framework unifies many previously unrelated tools and techniques and makes them understandable to a larger portion of the public. This gives the more practical-minded engineer, student and the industrial public an easy-access and more sensible road map into the world of machine learning. Machine Learning: Discriminative and Generative is designed for an audience composed of researchers & practitioners in industry and academia. The book is also suitable as a secondary text for graduate-level students in computer science and engineering.

Machine Learning

Machine Learning
Author : Ryszard S. Michalski,Jaime G. Carbonell,Tom M. Mitchell
Publisher : Elsevier
Release Date : 2014-06-28
Category : Computers
Total pages :572
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Machine Learning: An Artificial Intelligence Approach contains tutorial overviews and research papers representative of trends in the area of machine learning as viewed from an artificial intelligence perspective. The book is organized into six parts. Part I provides an overview of machine learning and explains why machines should learn. Part II covers important issues affecting the design of learning programs—particularly programs that learn from examples. It also describes inductive learning systems. Part III deals with learning by analogy, by experimentation, and from experience. Parts IV and V discuss learning from observation and discovery, and learning from instruction, respectively. Part VI presents two studies on applied learning systems—one on the recovery of valuable information via inductive inference; the other on inducing models of simple algebraic skills from observed student performance in the context of the Leeds Modeling System (LMS). This book is intended for researchers in artificial intelligence, computer science, and cognitive psychology; students in artificial intelligence and related disciplines; and a diverse range of readers, including computer scientists, robotics experts, knowledge engineers, educators, philosophers, data analysts, psychologists, and electronic engineers.

ABSOLUTE MACHINE LEARNING

ABSOLUTE MACHINE LEARNING
Author : Prachi Jain
Publisher : Prachi Jain
Release Date : 2020-07-27
Category : Computers
Total pages :53
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Machine learning is a subject of the 21st Century. Nothing can be more exciting than teaching human work to the computers. The objective of this book is to help the readers learn various concepts of machine learning in brief so that they can answer them in an interview or revise them before their written exams or viva voce. This book is not a substitute to the detailed concept explained in the books mentioned in bibliography, but just an aid during your interview preparation. Also, this book can never replace your practice of implementing the machine learning algorithms to solve the real-life problems in the world. I heartily wish you all the best for all your endeavours in learning and mastering the beautiful subject of machine learning. I invite you to join hands and contribute to the advancements in artificial intelligence; Thus, taking human civilization to the next level.

Data Analysis, Machine Learning and Applications

Data Analysis, Machine Learning and Applications
Author : Christine Preisach,Hans Burkhardt,Lars Schmidt-Thieme,Reinhold Decker
Publisher : Springer Science & Business Media
Release Date : 2008-04-13
Category : Computers
Total pages :719
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Data analysis and machine learning are research areas at the intersection of computer science, artificial intelligence, mathematics and statistics. They cover general methods and techniques that can be applied to a vast set of applications such as web and text mining, marketing, medical science, bioinformatics and business intelligence. This volume contains the revised versions of selected papers in the field of data analysis, machine learning and applications presented during the 31st Annual Conference of the German Classification Society (Gesellschaft für Klassifikation - GfKl). The conference was held at the Albert-Ludwigs-University in Freiburg, Germany, in March 2007.

Machine Learning Techniques for Multimedia

Machine Learning Techniques for Multimedia
Author : Matthieu Cord,Pádraig Cunningham
Publisher : Springer Science & Business Media
Release Date : 2008-02-07
Category : Computers
Total pages :289
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Processing multimedia content has emerged as a key area for the application of machine learning techniques, where the objectives are to provide insight into the domain from which the data is drawn, and to organize that data and improve the performance of the processes manipulating it. Arising from the EU MUSCLE network, this multidisciplinary book provides a comprehensive coverage of the most important machine learning techniques used and their application in this domain.