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Affiner la rechercheDEEP LEARNING with Python / François Chollet
Titre : DEEP LEARNING with Python Type de document : texte imprimé Auteurs : François Chollet, Auteur Mention d'édition : Second edition Editeur : Manning Publications Co. Année de publication : 2021 Importance : xxiv, 478 pages Présentation : illustrations (some color)., Format : 23 cm ISBN/ISSN/EAN : 978-1-617-29686-4 Langues : Anglais (eng) Catégories : 005.133 Python (Computer program language) Mots-clés : Deep learning with Python Index. décimale : 005.133 Résumé : Deep Learning with Python,Second Edition introduces the filed of deep learning using Python and the powerful keras library.In this revised and expanded new edition,keras creator François Chollet offers insights for both novice and experience machine learning practioners.As you move through this book, you 'll build your understand through intuitive explanations,crisp illustrations,and clear examples.You 'll quickly pick pick up the skills you need to start developing deep learning appications. DEEP LEARNING with Python [texte imprimé] / François Chollet, Auteur . - Second edition . - [S.l.] : Manning Publications Co., 2021 . - xxiv, 478 pages : illustrations (some color)., ; 23 cm.
ISBN : 978-1-617-29686-4
Langues : Anglais (eng)
Catégories : 005.133 Python (Computer program language) Mots-clés : Deep learning with Python Index. décimale : 005.133 Résumé : Deep Learning with Python,Second Edition introduces the filed of deep learning using Python and the powerful keras library.In this revised and expanded new edition,keras creator François Chollet offers insights for both novice and experience machine learning practioners.As you move through this book, you 'll build your understand through intuitive explanations,crisp illustrations,and clear examples.You 'll quickly pick pick up the skills you need to start developing deep learning appications. Exemplaires
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Titre : Deep learning Type de document : texte imprimé Auteurs : Ian Goodfellow, Auteur; Yoshua Bengio, Auteur; Aaron Courville, Auteur Editeur : China Année de publication : 2006 Importance : xxii, 775 pages Présentation : illustrations (some color) Format : 23cm ISBN/ISSN/EAN : 978-0-262-03561-3 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Deep learning Index. décimale : 006.31 Résumé : "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 video games. 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 [texte imprimé] / Ian Goodfellow, Auteur; Yoshua Bengio, Auteur; Aaron Courville, Auteur . - [S.l.] : China, 2006 . - xxii, 775 pages : illustrations (some color) ; 23cm.
ISBN : 978-0-262-03561-3
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Deep learning Index. décimale : 006.31 Résumé : "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 video games. 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" Exemplaires
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Titre : Natural language processing with PyTorch : Build intelligent language applications using deep learning. Type de document : texte imprimé Auteurs : Delip Rao, Auteur; Brian McMahan, Auteur Editeur : Beijing : O'Reilly Année de publication : 2019 Importance : 238 Page Présentation : ill Format : 24cm ISBN/ISSN/EAN : 978-1-491-97823-8 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Natural language processing with PyTorch : build intelligent language applications using deep learning Index. décimale : 006.35 Natural language processing with PyTorch [texte imprimé] : Build intelligent language applications using deep learning. / Delip Rao, Auteur; Brian McMahan, Auteur . - Beijing : O'Reilly, 2019 . - 238 Page : ill ; 24cm.
ISBN : 978-1-491-97823-8
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Natural language processing with PyTorch : build intelligent language applications using deep learning Index. décimale : 006.35 Exemplaires
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Titre : PRINCIPLES AND LABS FOR DEEP LEARNING Type de document : texte imprimé Auteurs : Shih-Chia Huang, Auteur; Trung-Hieu Le, Auteur Mention d'édition : 7 Editions Editeur : Elsevier Inc. All rights rights reserved Année de publication : 2021 Importance : ix 337 Pages Présentation : illustrator Black and whit Color Format : 27.50 Cm ISBN/ISSN/EAN : 978-0-323-90198-7 Langues : Anglais (eng) Langues originales : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : PRINCIPLES AND LABS FOR DEEP LEARNING Index. décimale : 006.31 Résumé : In 1943, Warren McCulloch and Walter Pitts introduced a computational model base on a threshold logic algorithm, Paving the way for the development of artificial intelligence (AI). The field of IA research was founded at the Dartmouth summer research project on artificial intelligence in 1956. In the nearly 80 years of development history, IA has experiences many ups and downs, especially the two "AI winter ," which are known as the periods of reduced funding and interesting AI research. PRINCIPLES AND LABS FOR DEEP LEARNING [texte imprimé] / Shih-Chia Huang, Auteur; Trung-Hieu Le, Auteur . - 7 Editions . - [S.l.] : Elsevier Inc. All rights rights reserved, 2021 . - ix 337 Pages : illustrator Black and whit Color ; 27.50 Cm.
ISBN : 978-0-323-90198-7
Langues : Anglais (eng) Langues originales : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : PRINCIPLES AND LABS FOR DEEP LEARNING Index. décimale : 006.31 Résumé : In 1943, Warren McCulloch and Walter Pitts introduced a computational model base on a threshold logic algorithm, Paving the way for the development of artificial intelligence (AI). The field of IA research was founded at the Dartmouth summer research project on artificial intelligence in 1956. In the nearly 80 years of development history, IA has experiences many ups and downs, especially the two "AI winter ," which are known as the periods of reduced funding and interesting AI research. Exemplaires
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Titre : AI and machine learning for on-device development : a programmer's guide Type de document : texte imprimé Auteurs : Laurence Moroney, Auteur Mention d'édition : First edition Editeur : Sebastopol, CA : O'Reilly Media Année de publication : 2021 Importance : xv, 309 pages Présentation : illustrations Format : 24 cm ISBN/ISSN/EAN : 978-1-09-810174-9 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : AI and machine learning for on-device development Index. décimale : 006.31 Résumé : Chapter 2. Introduction to Computer Vision -- Using Neurons for Vision -- Your First Classifier: Recognizing Clothing Items -- The Data: Fashion MNIST -- A Model Architecture to Parse Fashion MNIST -- Coding the Fashion MNIST Model -- Transfer Learning for Computer Vision -- Summary -- Chapter 3. Introduction to ML Kit -- Building a Face Detection App on Android -- Step 1: Create the App with Android Studio -- Step 2: Add and Configure ML Kit -- Step 3: Define the User Interface -- Step 4: Add the Images as Assets -- Step 5: Load the UI with a Default Picture AI and machine learning for on-device development [texte imprimé] : a programmer's guide / Laurence Moroney, Auteur . - First edition . - Sebastopol, CA : O'Reilly Media, 2021 . - xv, 309 pages : illustrations ; 24 cm.
ISBN : 978-1-09-810174-9
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : AI and machine learning for on-device development Index. décimale : 006.31 Résumé : Chapter 2. Introduction to Computer Vision -- Using Neurons for Vision -- Your First Classifier: Recognizing Clothing Items -- The Data: Fashion MNIST -- A Model Architecture to Parse Fashion MNIST -- Coding the Fashion MNIST Model -- Transfer Learning for Computer Vision -- Summary -- Chapter 3. Introduction to ML Kit -- Building a Face Detection App on Android -- Step 1: Create the App with Android Studio -- Step 2: Add and Configure ML Kit -- Step 3: Define the User Interface -- Step 4: Add the Images as Assets -- Step 5: Load the UI with a Default Picture Exemplaires
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Titre : AI and Machine Learning for Network and Security Management Type de document : texte imprimé Auteurs : Yulei Wu, Auteur Editeur : Wiley-IEEE Press Année de publication : 2023 Importance : xix, 263 pages Présentation : illustrations (graph), diagram Format : 24 cm ISBN/ISSN/EAN : 978-1-11-983587-5 Langues : Anglais (eng) Catégories : 006 Special computer methods Mots-clés : AI and Machine Learning for Network and Security Management Index. décimale : 006.76 Résumé : Al and Machine Learning for Network and Security Management covers a range of key topics of
network automation for network and security management, including resource allocation and
scheduling, network planning and routing, encrypted traffic classification, anomaly detection,
and security operations. In addition, the authors introduce their large-scale intelligent network
management and operation system and elaborate on how the aforementioned areas can be
integrated into this system, plus how the network service can benefit.AI and Machine Learning for Network and Security Management [texte imprimé] / Yulei Wu, Auteur . - [S.l.] : Wiley-IEEE Press, 2023 . - xix, 263 pages : illustrations (graph), diagram ; 24 cm.
ISBN : 978-1-11-983587-5
Langues : Anglais (eng)
Catégories : 006 Special computer methods Mots-clés : AI and Machine Learning for Network and Security Management Index. décimale : 006.76 Résumé : Al and Machine Learning for Network and Security Management covers a range of key topics of
network automation for network and security management, including resource allocation and
scheduling, network planning and routing, encrypted traffic classification, anomaly detection,
and security operations. In addition, the authors introduce their large-scale intelligent network
management and operation system and elaborate on how the aforementioned areas can be
integrated into this system, plus how the network service can benefit.Exemplaires
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Titre : Bayesian reasoning and machine learning Type de document : texte imprimé Auteurs : David BARBER, Auteur Editeur : Cambridge, UK ; New York : Cambridge University Press Importance : 697p Présentation : ill Format : 26cm ISBN/ISSN/EAN : 978-0-521-51814-7 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Machine Learning,Baysian Index. décimale : 006.31 Bayesian reasoning and machine learning [texte imprimé] / David BARBER, Auteur . - [S.l.] : Cambridge, UK ; New York : Cambridge University Press, [s.d.] . - 697p : ill ; 26cm.
ISBN : 978-0-521-51814-7
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Machine Learning,Baysian Index. décimale : 006.31 Exemplaires
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Titre : Data smart : using data science to transform information into insight Type de document : texte imprimé Auteurs : John W. Foreman, Auteur Editeur : John Wiley & Sons, Inc. Année de publication : 2014 Importance : xx, 409 pages Présentation : illustrations Format : 24.8 cm ISBN/ISSN/EAN : 978-1-11-866146-8 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Data smart, AI, Machine learning Index. décimale : 006.31 Résumé : "Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet."-- Publisher's description Data smart [texte imprimé] : using data science to transform information into insight / John W. Foreman, Auteur . - Canada : John Wiley & Sons, Inc., 2014 . - xx, 409 pages : illustrations ; 24.8 cm.
ISBN : 978-1-11-866146-8
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Data smart, AI, Machine learning Index. décimale : 006.31 Résumé : "Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet."-- Publisher's description Exemplaires
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Titre : The elements of statistical learning : data mining, inference, and prediction Type de document : texte imprimé Auteurs : Trevor Hastie, Auteur; Robert Tibshirani, Auteur Mention d'édition : second edition Editeur : Springer Science + Business Media, Inc. Année de publication : 2017 Importance : xxii, 745 pages Présentation : illustrations (some colors) Format : 24 cm ISBN/ISSN/EAN : 978-0-387-84857-0 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : The elements of statistical learning Index. décimale : 006.312 Résumé : "During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."--Jacket The elements of statistical learning [texte imprimé] : data mining, inference, and prediction / Trevor Hastie, Auteur; Robert Tibshirani, Auteur . - second edition . - [S.l.] : Springer Science + Business Media, Inc., 2017 . - xxii, 745 pages : illustrations (some colors) ; 24 cm.
ISBN : 978-0-387-84857-0
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : The elements of statistical learning Index. décimale : 006.312 Résumé : "During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics."--Jacket Exemplaires
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Titre : Foundations of machine learning Type de document : texte imprimé Auteurs : Mehryar MOHRI, Auteur; Afshin ROSTAMIZADEH, Auteur; Ameet TALWALKAR, Auteur; Ameet TALWALKAR Editeur : Massachusetts Institute of Technology Année de publication : 2012 Importance : xii, 414 p. : ill. Présentation : ill,. Format : 23cm. ISBN/ISSN/EAN : 978-0-262-01825-8 Note générale : Includes bibliographical references and index. Langues : Anglais (eng) Catégories : 000 Généralités:Méthodes informatiques spéciales:Intelligence artificielle Mots-clés : Machine learning.
Computer algorithmsIndex. décimale : 006.3 Foundations of machine learning [texte imprimé] / Mehryar MOHRI, Auteur; Afshin ROSTAMIZADEH, Auteur; Ameet TALWALKAR, Auteur; Ameet TALWALKAR . - [S.l.] : Massachusetts Institute of Technology, 2012 . - xii, 414 p. : ill. : ill,. ; 23cm..
ISBN : 978-0-262-01825-8
Includes bibliographical references and index.
Langues : Anglais (eng)
Catégories : 000 Généralités:Méthodes informatiques spéciales:Intelligence artificielle Mots-clés : Machine learning.
Computer algorithmsIndex. décimale : 006.3 Réservation
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Titre : Fundamentals of deep excavations Type de document : texte imprimé Auteurs : Chang-Yu Ou, Auteur Mention d'édition : Editions:6 Editeur : Taylor & Francis Group Année de publication : 2022 Importance : xii 469 Pages Présentation : illustrations black and white Format : 17.50cmx25cm ISBN/ISSN/EAN : 978-0-367-42601-9 Langues : Anglais (eng) Langues originales : Anglais (eng) Catégories : 624.152 Explosives. Mots-clés : Fundamentals of deep excavations Index. décimale : 624.152 Résumé : "Excavation is an important segment of foundation engineering (for example, in the construction of the foundations or basements of high rise buildings, underground oil tanks, or subways). However, the excavation knowledge introduced in most books on foundation engineering is too simple to handle actual excavation analysis and design. Moreover, with economic development and urbanization, excavations go deeper and are larger in scale. These conditions require elaborate analysis and design methods and construction technologies. This book is aimed at both theoretical explication and practical application. From basic to advanced, this book attempts to achieve theoretical rigorous and consistency. Each chapter is followed by a problem set so that the book can be readily taught at senior undergraduate and graduate levels. The solution to the problems at the end of the chapters can be found on the website (http://www.ct.ntust.edu.tw/ou/). On the other hand, the analysis methods introduced in the book can be used in actual analysis and design as they contain the most up-to-date knowledge. Therefore, this book is suitable for teachers who teach foundation engineering and/or deep excavation courses and engineers who are engaged in excavation analysis and design"-- Provided by publisher
Fundamentals of deep excavations [texte imprimé] / Chang-Yu Ou, Auteur . - Editions:6 . - [S.l.] : Taylor & Francis Group, 2022 . - xii 469 Pages : illustrations black and white ; 17.50cmx25cm.
ISBN : 978-0-367-42601-9
Langues : Anglais (eng) Langues originales : Anglais (eng)
Catégories : 624.152 Explosives. Mots-clés : Fundamentals of deep excavations Index. décimale : 624.152 Résumé : "Excavation is an important segment of foundation engineering (for example, in the construction of the foundations or basements of high rise buildings, underground oil tanks, or subways). However, the excavation knowledge introduced in most books on foundation engineering is too simple to handle actual excavation analysis and design. Moreover, with economic development and urbanization, excavations go deeper and are larger in scale. These conditions require elaborate analysis and design methods and construction technologies. This book is aimed at both theoretical explication and practical application. From basic to advanced, this book attempts to achieve theoretical rigorous and consistency. Each chapter is followed by a problem set so that the book can be readily taught at senior undergraduate and graduate levels. The solution to the problems at the end of the chapters can be found on the website (http://www.ct.ntust.edu.tw/ou/). On the other hand, the analysis methods introduced in the book can be used in actual analysis and design as they contain the most up-to-date knowledge. Therefore, this book is suitable for teachers who teach foundation engineering and/or deep excavation courses and engineers who are engaged in excavation analysis and design"-- Provided by publisher
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Titre : Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow : Concepts, tools, and techniques to build intelligent systems Type de document : texte imprimé Auteurs : Géron Aurélien, Auteur Mention d'édition : Third Edition Editeur : O'Reilly Media, Inc. Année de publication : 2023 Importance : xxv 834 Pages Présentation : illustrations, (Some Color) Format : 18cmx32cm ISBN/ISSN/EAN : 978-1-09-812597-4 Langues : Anglais (eng) Langues originales : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow Index. décimale : 006.31 Résumé : Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow [texte imprimé] : Concepts, tools, and techniques to build intelligent systems / Géron Aurélien, Auteur . - Third Edition . - [S.l.] : O'Reilly Media, Inc., 2023 . - xxv 834 Pages : illustrations, (Some Color) ; 18cmx32cm.
ISBN : 978-1-09-812597-4
Langues : Anglais (eng) Langues originales : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow Index. décimale : 006.31 Résumé : Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released Exemplaires
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Titre : Introduction to data science : a Python approach to concepts, techniques and applications Type de document : texte imprimé Auteurs : Laura Igual Muñoz, Auteur; Santi Seguí, Auteur Editeur : Berlin ; New York : Springer, Année de publication : 2017 Importance : 218 Page Présentation : ill Format : 24cm ISBN/ISSN/EAN : 978-3-319-50016-4 Langues : Anglais (eng) Catégories : 001.642 Computer programming -- Psychological aspects. Mots-clés : Introduction to data science : a Python approach to concepts, techniques and applications' ' Index. décimale : 001.42 Introduction to data science : a Python approach to concepts, techniques and applications [texte imprimé] / Laura Igual Muñoz, Auteur; Santi Seguí, Auteur . - [S.l.] : Berlin ; New York : Springer,, 2017 . - 218 Page : ill ; 24cm.
ISBN : 978-3-319-50016-4
Langues : Anglais (eng)
Catégories : 001.642 Computer programming -- Psychological aspects. Mots-clés : Introduction to data science : a Python approach to concepts, techniques and applications' ' Index. décimale : 001.42 Exemplaires
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Titre : An introduction to statistical learning : with applications in R Type de document : texte imprimé Auteurs : James, Gareth (Gareth Michael), Auteur; Trevor Hastie, Auteur; Witten, Daniela, Auteur Mention d'édition : Second edition Editeur : New York : Springer Année de publication : 2021 Importance : XV, 607p Présentation : illustrations (some color)., Format : 23 ISBN/ISSN/EAN : 978-1-07-161417-4 Langues : Anglais (eng) Catégories : 540 Chimie et sciences connexes:500 Sciences de la nature et mathématiques:519 Probabilités et mathématiques appliquées:519.5 Statistique mathématique Mots-clés : An introduction to statistical learning Index. décimale : 519.5 Résumé : An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility
An introduction to statistical learning [texte imprimé] : with applications in R / James, Gareth (Gareth Michael), Auteur; Trevor Hastie, Auteur; Witten, Daniela, Auteur . - Second edition . - New York : Springer, 2021 . - XV, 607p : illustrations (some color)., ; 23.
ISBN : 978-1-07-161417-4
Langues : Anglais (eng)
Catégories : 540 Chimie et sciences connexes:500 Sciences de la nature et mathématiques:519 Probabilités et mathématiques appliquées:519.5 Statistique mathématique Mots-clés : An introduction to statistical learning Index. décimale : 519.5 Résumé : An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naive Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility
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Titre : Learning jQuery 3 : build interesting, interactive sites using jQuery by automating common tasks and simplifying the complicated ones Type de document : texte imprimé Auteurs : Adam. Boduch, Auteur; Jonathan. Chaffer, Auteur; Karl. Swedberg, Auteur Editeur : Birmingham, U.K. : Packt Publishing Ltd. Année de publication : 2017 Importance : 425 Pages Présentation : Black and White Format : 23.50cmx19cm ISBN/ISSN/EAN : 978-1-7858-8298-2 Langues : Anglais (eng) Mots-clés : Learning jquery3 Fifth Edition By Adam Boduch. Index. décimale : 005.2762 Learning jQuery 3 [texte imprimé] : build interesting, interactive sites using jQuery by automating common tasks and simplifying the complicated ones / Adam. Boduch, Auteur; Jonathan. Chaffer, Auteur; Karl. Swedberg, Auteur . - Birmingham, U.K. : Packt Publishing Ltd., 2017 . - 425 Pages : Black and White ; 23.50cmx19cm.
ISBN : 978-1-7858-8298-2
Langues : Anglais (eng)
Mots-clés : Learning jquery3 Fifth Edition By Adam Boduch. Index. décimale : 005.2762 Exemplaires
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Titre : Learning Python Type de document : texte imprimé Auteurs : LUTZ, Mark., Auteur Mention d'édition : 5th ed.. Editeur : Beijing : O'Reilly Année de publication : 2013 Importance : l, 1540 pages Présentation : ill. Format : 24cm. ISBN/ISSN/EAN : 978-1-449-35573-9 Note générale : "Updated for 3.3 and 2.7"--Cover.
Includes indexLangues : Anglais (eng) Catégories : 000 Généralités:Programmation, programmes, organisation des données, logiciel:Programmation Mots-clés : Python (Computer program language)
Object-oriented programming (Computer science)Index. décimale : 005.1 Learning Python [texte imprimé] / LUTZ, Mark., Auteur . - 5th ed.. . - Beijing : O'Reilly, 2013 . - l, 1540 pages : ill. ; 24cm..
ISBN : 978-1-449-35573-9
"Updated for 3.3 and 2.7"--Cover.
Includes index
Langues : Anglais (eng)Exemplaires
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Titre : Linear algebra and learning from data Type de document : texte imprimé Auteurs : Gilbert Strang, Auteur; Kiminori Matsuzaki, Traducteur Editeur : Wellesley-Cambridge Press, Wellesley, MA. Année de publication : 2019 Importance : xiii 432 Pages Présentation : illustrations black and white Format : 19.50cmx24cm ISBN/ISSN/EAN : 978-0-692-19638-0 Langues : Anglais (eng) Langues originales : Anglais (eng) Catégories : 540 Chimie et sciences connexes:500 Sciences de la nature et mathématiques:512 Algèbre et théorie des nombres:512.5 Algèbres linéaire, multilinéaire, multidimensionnelle Mots-clés : Linear algebra and learning from data Index. décimale : 512.5 Résumé : This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data. Audience: This book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods. Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch (the four fundamental subspaces) and is fully accessible without the first text. -- Publisher’s description Linear algebra and learning from data [texte imprimé] / Gilbert Strang, Auteur; Kiminori Matsuzaki, Traducteur . - [S.l.] : Wellesley-Cambridge Press, Wellesley, MA., 2019 . - xiii 432 Pages : illustrations black and white ; 19.50cmx24cm.
ISBN : 978-0-692-19638-0
Langues : Anglais (eng) Langues originales : Anglais (eng)
Catégories : 540 Chimie et sciences connexes:500 Sciences de la nature et mathématiques:512 Algèbre et théorie des nombres:512.5 Algèbres linéaire, multilinéaire, multidimensionnelle Mots-clés : Linear algebra and learning from data Index. décimale : 512.5 Résumé : This is a textbook to help readers understand the steps that lead to deep learning. Linear algebra comes first especially singular values, least squares, and matrix factorizations. Often the goal is a low rank approximation A = CR (column-row) to a large matrix of data to see its most important part. This uses the full array of applied linear algebra, including randomization for very large matrices. Then deep learning creates a large-scale optimization problem for the weights solved by gradient descent or better stochastic gradient descent. Finally, the book develops the architectures of fully connected neural nets and of Convolutional Neural Nets (CNNs) to find patterns in data. Audience: This book is for anyone who wants to learn how data is reduced and interpreted by and understand matrix methods. Based on the second linear algebra course taught by Professor Strang, whose lectures on the training data are widely known, it starts from scratch (the four fundamental subspaces) and is fully accessible without the first text. -- Publisher’s description Exemplaires
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Titre : Machine Learning Type de document : texte imprimé Auteurs : Michael B. White, Auteur Editeur : Michael B White Année de publication : 2008 Importance : 311 pages Présentation : illustrations ISBN/ISSN/EAN : 978-1-7234-8472-8 Langues : Français (fre) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Machine Learning Index. décimale : 006.31 Machine Learning [texte imprimé] / Michael B. White, Auteur . - [S.l.] : Michael B White, 2008 . - 311 pages : illustrations.
ISBN : 978-1-7234-8472-8
Langues : Français (fre)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Machine Learning Index. décimale : 006.31 Réservation
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Titre : Machine learning with PyTorch and Scikit-Learn : develop machine learning and deep learning models with Python Type de document : texte imprimé Auteurs : Sebastian Raschka, Auteur; Yuxi Liu, Auteur; Vahid Mirjalili, Auteur Editeur : Birmingham : Packt Publishing Année de publication : 2022 Importance : xxix, 741 pages Présentation : illustrations, graphs, charts Format : 26 cm ISBN/ISSN/EAN : 978-1-8018-1931-2 Langues : Anglais (eng) Catégories : 005 Computer programming, programs, data Mots-clés : Machine learning with PyTorch and Scikit-Learn Index. décimale : 005.133 Résumé : Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. Machine learning with PyTorch and Scikit-Learn [texte imprimé] : develop machine learning and deep learning models with Python / Sebastian Raschka, Auteur; Yuxi Liu, Auteur; Vahid Mirjalili, Auteur . - Birmingham : Packt Publishing, 2022 . - xxix, 741 pages : illustrations, graphs, charts ; 26 cm.
ISBN : 978-1-8018-1931-2
Langues : Anglais (eng)
Catégories : 005 Computer programming, programs, data Mots-clés : Machine learning with PyTorch and Scikit-Learn Index. décimale : 005.133 Résumé : Machine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments. Exemplaires
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Titre : Machine learning security principles : use various methods to keep data, networks, users, and applications safe from prying eyes Type de document : texte imprimé Auteurs : John Paul Mueller, Auteur Mention d'édition : First Edition Editeur : Birmingham : Packt Publishing Année de publication : 2022 Importance : xxiv, 425 pages Présentation : illustrations Format : 23 cm ISBN/ISSN/EAN : 978-1-8046-1885-1 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Machine learning security principles Index. décimale : 006.31 Machine learning security principles [texte imprimé] : use various methods to keep data, networks, users, and applications safe from prying eyes / John Paul Mueller, Auteur . - First Edition . - Birmingham : Packt Publishing, 2022 . - xxiv, 425 pages : illustrations ; 23 cm.
ISBN : 978-1-8046-1885-1
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Machine learning security principles Index. décimale : 006.31 Exemplaires
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Titre : Mapping leadership : the tasks that matter for improving teaching and learning in schools Type de document : texte imprimé Auteurs : Richard Halverson, Auteur; Carolyn Kelley, Auteur Editeur : Jossey-Bass, a Wiley brand, San Francisco, CA Année de publication : 2017 Importance : xiv, 209 pages Format : 24cm ISBN/ISSN/EAN : 978-1-11-871169-9 Langues : Anglais (eng) Catégories : 300 Sciences sociales:371 Les écoles et leurs activités, enseignement spécialisé, éducation spéciale:371.2 Administration scolaire Mots-clés : Mapping leadership : the tasks that matter for improving teaching and learning in schools Index. décimale : 371.2 Résumé : "Drawing on twenty years of research in schoool effectiveness, this book presents a distributed model of school leadership that leads to continuous school improvement ... Recognizing that the principal is one actor in a complex web of activity, the focus is on a range of leadership and instructional practices to be shared across the team ... The book is connected to the CALL (comprehensive assessment of leadership for learning), the in-depth assessment that leadership teams use to diagnose a school's effectiveness"--Back cover Mapping leadership : the tasks that matter for improving teaching and learning in schools [texte imprimé] / Richard Halverson, Auteur; Carolyn Kelley, Auteur . - [S.l.] : Jossey-Bass, a Wiley brand, San Francisco, CA, 2017 . - xiv, 209 pages ; 24cm.
ISBN : 978-1-11-871169-9
Langues : Anglais (eng)
Catégories : 300 Sciences sociales:371 Les écoles et leurs activités, enseignement spécialisé, éducation spéciale:371.2 Administration scolaire Mots-clés : Mapping leadership : the tasks that matter for improving teaching and learning in schools Index. décimale : 371.2 Résumé : "Drawing on twenty years of research in schoool effectiveness, this book presents a distributed model of school leadership that leads to continuous school improvement ... Recognizing that the principal is one actor in a complex web of activity, the focus is on a range of leadership and instructional practices to be shared across the team ... The book is connected to the CALL (comprehensive assessment of leadership for learning), the in-depth assessment that leadership teams use to diagnose a school's effectiveness"--Back cover Exemplaires
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Titre : MODELING AND SIMULATION IN PYTHON : AN INTRODUCTION FOR SCIENTISTS AND ENGINEERS Type de document : texte imprimé Auteurs : Allen Downey, Auteur Editeur : O'REILLY MEDIA Année de publication : 2023 Importance : 248 pages Présentation : illustrations(grap..) Format : 23cm ISBN/ISSN/EAN : 978-1-7185-0216-1 Langues : Anglais (eng) Catégories : 005 Computer programming, programs, data Mots-clés : MODELING AND SIMULATION IN PYTHON Index. décimale : 005 MODELING AND SIMULATION IN PYTHON [texte imprimé] : AN INTRODUCTION FOR SCIENTISTS AND ENGINEERS / Allen Downey, Auteur . - [S.l.] : O'REILLY MEDIA, 2023 . - 248 pages : illustrations(grap..) ; 23cm.
ISBN : 978-1-7185-0216-1
Langues : Anglais (eng)
Catégories : 005 Computer programming, programs, data Mots-clés : MODELING AND SIMULATION IN PYTHON Index. décimale : 005 Exemplaires
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Titre : Pattern recognition and machine learning Type de document : texte imprimé Auteurs : Christopher M Bishop, Auteur Editeur : Springer Science+Business Media ,LLC Année de publication : 2006 Importance : xx,738 pages Présentation : illustrations (some colors) Format : 25 cm ISBN/ISSN/EAN : 978-1-493-93843-8 Langues : Anglais (eng) Catégories : 006.4 Machine learning Mots-clés : Pattern recognition and machine learning Index. décimale : 006.4 Résumé : "The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information." Pattern recognition and machine learning [texte imprimé] / Christopher M Bishop, Auteur . - [S.l.] : Springer Science+Business Media ,LLC, 2006 . - xx,738 pages : illustrations (some colors) ; 25 cm.
ISBN : 978-1-493-93843-8
Langues : Anglais (eng)
Catégories : 006.4 Machine learning Mots-clés : Pattern recognition and machine learning Index. décimale : 006.4 Résumé : "The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information." Exemplaires
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Titre : Practical data science with Python 3 : Synthesizing actionable insights from data Type de document : texte imprimé Auteurs : Ervin Varga, Auteur Editeur : [New York] : Apress Année de publication : 2019 Importance : 462 Page Présentation : ill Format : 24cm ISBN/ISSN/EAN : 978-1-484-24858-4 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Practical data science with Python 3 : synthesizing actionable insights from data' ' Index. décimale : 006.312 Practical data science with Python 3 [texte imprimé] : Synthesizing actionable insights from data / Ervin Varga, Auteur . - [S.l.] : [New York] : Apress, 2019 . - 462 Page : ill ; 24cm.
ISBN : 978-1-484-24858-4
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Mots-clés : Practical data science with Python 3 : synthesizing actionable insights from data' ' Index. décimale : 006.312 Exemplaires
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Titre : Python for data analysis : data wrangling with Pandas, NumPy, and Jupyter Type de document : texte imprimé Auteurs : Wes McKinney, Auteur Mention d'édition : Third edition Editeur : O'Reilly Media, Inc. Année de publication : 2022 Importance : xvi, 561 pages Présentation : illustrations (graph) Format : 24 cm ISBN/ISSN/EAN : 978-1-09-810403-0 Langues : Anglais (eng) Mots-clés : Python for data analysis Index. décimale : 005.133 Résumé : Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process Python for data analysis [texte imprimé] : data wrangling with Pandas, NumPy, and Jupyter / Wes McKinney, Auteur . - Third edition . - [S.l.] : O'Reilly Media, Inc., 2022 . - xvi, 561 pages : illustrations (graph) ; 24 cm.
ISBN : 978-1-09-810403-0
Langues : Anglais (eng)
Mots-clés : Python for data analysis Index. décimale : 005.133 Résumé : Get the definitive handbook for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process Exemplaires
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Titre : Python for Data Analysis : Data Wrangling with pandas,Numpy & Jupyter Type de document : texte imprimé Auteurs : Wes McKinney, Auteur Mention d'édition : Third Edition Editeur : O'Reilly Media, Inc. Année de publication : 2022 Importance : xvi, 561 pages Présentation : illustrations black and white Format : 23 cm ISBN/ISSN/EAN : 978-1-09-810403-0 Langues : Anglais (eng) Catégories : 005.133 Data Analysis Mots-clés : Python for data analysis Index. décimale : 005.133 Résumé : Get the definitive handbook for manipulating, processing,cleaning,and crunching datasets in Python.Updated for Python 3.10 and pandas 1.4,the this hands on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively.you 'll learn the latest versions of pands,Numpy,and Jupyterin the process. Python for Data Analysis [texte imprimé] : Data Wrangling with pandas,Numpy & Jupyter / Wes McKinney, Auteur . - Third Edition . - [S.l.] : O'Reilly Media, Inc., 2022 . - xvi, 561 pages : illustrations black and white ; 23 cm.
ISBN : 978-1-09-810403-0
Langues : Anglais (eng)
Catégories : 005.133 Data Analysis Mots-clés : Python for data analysis Index. décimale : 005.133 Résumé : Get the definitive handbook for manipulating, processing,cleaning,and crunching datasets in Python.Updated for Python 3.10 and pandas 1.4,the this hands on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively.you 'll learn the latest versions of pands,Numpy,and Jupyterin the process. Exemplaires
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Titre : Python for data science : a hands-on introduction Type de document : texte imprimé Auteurs : Yuli Vasiliev, Auteur Editeur : San Francisco : No Starch Press Année de publication : 2022 Importance : xviii, 217 pages Présentation : illustrations Format : 24 cm ISBN/ISSN/EAN : 978-1-7185-0220-8 Langues : Anglais (eng) Catégories : 005 Computer programming, programs, data Mots-clés : Python for data science Index. décimale : 005.133 Résumé : Shows programmers the best ways to leverage Python for data-driven applications. Loaded with practical examples, the book provides a wide-ranging tour of Python's abilities to obtain, transform, and analyze data"-- Provided by publisher Python for data science [texte imprimé] : a hands-on introduction / Yuli Vasiliev, Auteur . - San Francisco : No Starch Press, 2022 . - xviii, 217 pages : illustrations ; 24 cm.
ISBN : 978-1-7185-0220-8
Langues : Anglais (eng)
Catégories : 005 Computer programming, programs, data Mots-clés : Python for data science Index. décimale : 005.133 Résumé : Shows programmers the best ways to leverage Python for data-driven applications. Loaded with practical examples, the book provides a wide-ranging tour of Python's abilities to obtain, transform, and analyze data"-- Provided by publisher Exemplaires
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Titre : Structural health monitoring : a machine learning perspective Type de document : texte imprimé Auteurs : C. R. (Charles R.) Farrar, Auteur; Worden Keith, Auteur Editeur : John Wiley & Sons, Ltd Année de publication : 2013 Importance : 631 Pages Présentation : ill (graph...) Format : 26cm ISBN/ISSN/EAN : 978-1-11-999433-6 Langues : Anglais (eng) Catégories : 624.171 Structural analysis (Engineering) -- Data processing. Mots-clés : Structural health monitoring : a machine learning perspective' ' Index. décimale : 624.171 Structural health monitoring : a machine learning perspective [texte imprimé] / C. R. (Charles R.) Farrar, Auteur; Worden Keith, Auteur . - [S.l.] : John Wiley & Sons, Ltd, 2013 . - 631 Pages : ill (graph...) ; 26cm.
ISBN : 978-1-11-999433-6
Langues : Anglais (eng)
Catégories : 624.171 Structural analysis (Engineering) -- Data processing. Mots-clés : Structural health monitoring : a machine learning perspective' ' Index. décimale : 624.171 Exemplaires
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