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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 : 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
Code-barres Cote Support Localisation Section Disponibilité 116008 006.31 BAR Livre Library Repository Exclu du prêt
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 : 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 : 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 : 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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Code-barres Cote Support Localisation Section Disponibilité 0-118871 006.31 WHI Livre Library Repository Exclu du prêt 0-119902 006.31 WHI Livre Library Shelf Sorti jusqu'au 21/10/2024 0-119886 006.31 WHI Thesis Library Shelf Disponible
Titre : Machine learning : a probabilistic perspective Type de document : texte imprimé Auteurs : Kevin P. MURPHY, Auteur Editeur : Cambridge, Mass. : MIT Press Année de publication : 2012 Importance : 1071p Présentation : ill,.(some col) Format : 24cm ISBN/ISSN/EAN : 978-0-262-01802-9 Langues : Anglais (eng) Catégories : 000 Généralités:006.31 Machine Learning Index. décimale : 006.31 Machine learning : a probabilistic perspective [texte imprimé] / Kevin P. MURPHY, Auteur . - [S.l.] : Cambridge, Mass. : MIT Press, 2012 . - 1071p : ill,.(some col) ; 24cm.
ISBN : 978-0-262-01802-9
Langues : Anglais (eng)
Catégories : 000 Généralités:006.31 Machine Learning Index. décimale : 006.31 Exemplaires
Code-barres Cote Support Localisation Section Disponibilité 115997 006.31 MUR Livre Library Repository Exclu du prêt
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 : 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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