Introduction Au Machine Learning
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Introduction Au Deep Learning
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Author : Eugène Charniak
language : fr
Publisher:
Release Date : 2021-01-13
Introduction Au Deep Learning written by Eugène Charniak and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-13 with categories.
Introduction Au Machine Learning 2e D
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Author : Chloé-Agathe Azencott
language : fr
Publisher: Dunod
Release Date : 2022-02-02
Introduction Au Machine Learning 2e D written by Chloé-Agathe Azencott and has been published by Dunod this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with Mathematics categories.
Le machine learning (apprentissage automatique) est au coeur des data sciences et s'applique à une multitude de domaines tels que la reconnaissance des visages par ordinateur, la traduction automatique d'une langue à l'autre, la conduite automobile automatique, la publicité ciblée, l'analyse des réseaux sociaux, le trading financier, ... Ce livre propose une introduction aux concepts et aux algorithmes qui fondent le machine learning. Son objectif est de fournir au lecteur les outils pour : - identifier les problèmes qui peuvent être résolus par du machine learning, - formaliser ces problèmes en termes de machine learning, - identifier quels sont les algorithmes appropriés et apprendre à les mettre en oeuvre, - savoir évaluer et comparer les performances de plusieurs algorithmes. Chaque chapitre est complété par des exercices corrigés. Cette seconde édition a été complétée par de nouvelles méthodes comme le clustering spectral, le clustering par mélange de gaussiennes, et la réduction de dimension avec UMAP.
Introduction To Deep Learning
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Author : Eugene Charniak
language : en
Publisher: MIT Press
Release Date : 2019-01-29
Introduction To Deep Learning written by Eugene Charniak and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-29 with Computers categories.
A project-based guide to the basics of deep learning. This concise, project-driven guide to deep learning takes readers through a series of program-writing tasks that introduce them to the use of deep learning in such areas of artificial intelligence as computer vision, natural-language processing, and reinforcement learning. The author, a longtime artificial intelligence researcher specializing in natural-language processing, covers feed-forward neural nets, convolutional neural nets, word embeddings, recurrent neural nets, sequence-to-sequence learning, deep reinforcement learning, unsupervised models, and other fundamental concepts and techniques. Students and practitioners learn the basics of deep learning by working through programs in Tensorflow, an open-source machine learning framework. “I find I learn computer science material best by sitting down and writing programs,” the author writes, and the book reflects this approach. Each chapter includes a programming project, exercises, and references for further reading. An early chapter is devoted to Tensorflow and its interface with Python, the widely used programming language. Familiarity with linear algebra, multivariate calculus, and probability and statistics is required, as is a rudimentary knowledge of programming in Python. The book can be used in both undergraduate and graduate courses; practitioners will find it an essential reference.
Introduction To Machine Learning
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Author : Yves Kodratoff
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Introduction To Machine Learning written by Yves Kodratoff and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Computers categories.
A textbook suitable for undergraduate courses in machine learning and related topics, this book provides a broad survey of the field. Generous exercises and examples give students a firm grasp of the concepts and techniques of this rapidly developing, challenging subject. Introduction to Machine Learning synthesizes and clarifies the work of leading researchers, much of which is otherwise available only in undigested technical reports, journals, and conference proceedings. Beginning with an overview suitable for undergraduate readers, Kodratoff establishes a theoretical basis for machine learning and describes its technical concepts and major application areas. Relevant logic programming examples are given in Prolog. Introduction to Machine Learning is an accessible and original introduction to a significant research area.
Introduction Au Machine Learning
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Author : Chloé-Agathe Azencott
language : fr
Publisher:
Release Date : 2022-02-02
Introduction Au Machine Learning written by Chloé-Agathe Azencott and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-02 with categories.
Machine Learning Fundamentals
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Author : Hui Jiang
language : en
Publisher: Cambridge University Press
Release Date : 2021-11-25
Machine Learning Fundamentals written by Hui Jiang 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 2021-11-25 with Computers categories.
A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.
Introduction To Machine Learning Fourth Edition
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Author : Ethem Alpaydin
language : en
Publisher: MIT Press
Release Date : 2020-03-24
Introduction To Machine Learning Fourth Edition written by Ethem Alpaydin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-24 with Computers categories.
A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks. The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.
Informatique D Couverte Du Machine Learning Les Outils De L Apprentissage Automatique
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Author : Gérard Fleury
language : fr
Publisher: Editions Ellipses
Release Date : 2021-04-20
Informatique D Couverte Du Machine Learning Les Outils De L Apprentissage Automatique written by Gérard Fleury and has been published by Editions Ellipses this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-20 with Computers categories.
Cet ouvrage propose une découverte pragmatique du Machine Learning à travers des exemples implémentés. Il constitue une introduction à différentes méthodes permettant aux étudiants de DUT, de licence, des écoles d'ingénieurs et aux chercheurs de découvrir plusieurs aspects du domaine. Le domaine du Machine Learning couvre un large spectre d'outils et de méthodes. Cet ouvrage fait un focus particulier sur les réseaux de neurones, les réseaux Bayésiens, les méthodes de classification, le pattern mining et les séries temporelles. La découverte s'effectue en utilisant des bibliothèques dédiées au Machine Learning, notamment TensorFlow, Keras, pyAgrum et Weka. Les exemples du livre sont essentiellement des problèmes qui ont été tirés des domaines d'expertise des auteurs. Les codes informatiques sont proposés en Python, en C et en Java, car les domaines où le Machine Learning est utile sont très nombreux et il est important d'avoir une vue globale de ce qu'il est possible de faire avec les outils récents.
An Introduction To Machine Learning
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Author : Gopinath Rebala
language : en
Publisher: Springer
Release Date : 2019-05-07
An Introduction To Machine Learning written by Gopinath Rebala and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-07 with Technology & Engineering categories.
Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.
Introduction To Machine Learning Fourth Edition
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Author : Ethem Alpaydin
language : en
Publisher: MIT Press
Release Date : 2020-03-24
Introduction To Machine Learning Fourth Edition written by Ethem Alpaydin and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-24 with Computers categories.
A substantially revised fourth edition of a comprehensive textbook, including new coverage of recent advances in deep learning and neural networks. The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Machine learning underlies such exciting new technologies as self-driving cars, speech recognition, and translation applications. This substantially revised fourth edition of a comprehensive, widely used machine learning textbook offers new coverage of recent advances in the field in both theory and practice, including developments in deep learning and neural networks. The book covers a broad array of topics not usually included in introductory machine learning texts, including supervised learning, Bayesian decision theory, parametric methods, semiparametric methods, nonparametric methods, multivariate analysis, hidden Markov models, reinforcement learning, kernel machines, graphical models, Bayesian estimation, and statistical testing. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as convolutional and generative adversarial networks; new material in the chapter on reinforcement learning that covers the use of deep networks, the policy gradient methods, and deep reinforcement learning; new material in the chapter on multilayer perceptrons on autoencoders and the word2vec network; and discussion of a popular method of dimensionality reduction, t-SNE. New appendixes offer background material on linear algebra and optimization. End-of-chapter exercises help readers to apply concepts learned. Introduction to Machine Learning can be used in courses for advanced undergraduate and graduate students and as a reference for professionals.