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Titre : Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph- Based Techniques Type de document : texte imprimé Auteurs : Akshay Kulkarni Editeur : Apress Année de publication : 2022 Importance : 1 vol. (336 p.) Format : 24 cm ISBN/ISSN/EAN : 978-1-4842-8953-2 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé :
This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms. What You Will Learn Understand and implement different recommender systems techniques with Python Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is For Data scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.Note de contenu :
Cover
Front Matter
1. Introduction to Recommendation Systems
2. Market Basket Analysis (Association Rule Mining)
3. Content-Based Recommender Systems
4. Collaborative Filtering
5. Collaborative Filtering Using Matrix Factorization, Singular Value Decomposition, and Co-Clustering
6. Hybrid Recommender Systems
7. Clustering-Based Recommender Systems
8. Classification Algorithm–Based Recommender Systems
9. Deep Learning–Based Recommender System
10. Graph-Based Recommender Systems
11. Emerging Areas and Techniques in Recommender SystemsCôte titre : Fs/24986 Applied Recommender Systems with Python : Build Recommender Systems with Deep Learning, NLP and Graph- Based Techniques [texte imprimé] / Akshay Kulkarni . - Usa : Apress, 2022 . - 1 vol. (336 p.) ; 24 cm.
ISBN : 978-1-4842-8953-2
Langues : Anglais (eng)
Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé :
This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today. You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations. By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms. What You Will Learn Understand and implement different recommender systems techniques with Python Employ popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filtering Leverage machine learning, NLP, and deep learning for building recommender systems Who This Book Is For Data scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.Note de contenu :
Cover
Front Matter
1. Introduction to Recommendation Systems
2. Market Basket Analysis (Association Rule Mining)
3. Content-Based Recommender Systems
4. Collaborative Filtering
5. Collaborative Filtering Using Matrix Factorization, Singular Value Decomposition, and Co-Clustering
6. Hybrid Recommender Systems
7. Clustering-Based Recommender Systems
8. Classification Algorithm–Based Recommender Systems
9. Deep Learning–Based Recommender System
10. Graph-Based Recommender Systems
11. Emerging Areas and Techniques in Recommender SystemsCôte titre : Fs/24986 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/24986 Fs/24986 Livre Bibliothéque des sciences Anglais Disponible
Disponible
Titre : Beginning XML with DOM and Ajax : From Novice to Professional Type de document : texte imprimé Auteurs : Sas Jacobs Editeur : Apress Année de publication : 2006 Importance : 1 vol (432 p.) Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-59059-676-0 Langues : Français (fre) Catégories : Informatique Mots-clés : XML
DOM
AjaxIndex. décimale : 004 Informatique Résumé :
ce livre est tout ce dont vous avez besoin pour aller de l'avant dans le développement XML. Le développeur Web renommé Sas Jacobs présente un guide essentiel pour XML.
Commencer XML avec DOM et Ajax est pratique et complet. Il comprend tout ce dont vous avez besoin pour vous familiariser avec le développement XML rapidement et sans effort.
Jacobs commence par présenter une vue d'ensemble de XML - c'est la syntaxe, les règles, les vocabulaires, et le comment et le pourquoi de la validité. Elle couvre également l'état actuel du support XML dans les navigateurs Web d'aujourd'hui. Ensuite, Jacobs couvre toutes les utilisations essentielles de XML. Vous apprendrez à afficher des données XML à l'aide de CSS et à transformer des données XML à l'aide de XSLT. Vous apprendrez même à propos des scripts XML dynamiques utilisant le DOM XML.
La dernière partie du livre aborde les utilisations avancées du XML côté serveur dans des applications réelles, notamment l'affichage de données XML dans Flash et les applications PHP et ASP.NET basées sur XML. Enfin, Jacobs fournit une introduction parfaite au développement d'Ajax.Note de contenu :
Sommaire
Introduction to XML
Related XML recommendations
Web vocabularies
Client-side XML
Displaying XML using CSS
Introduction to XSLT
Advanced client-side XSLT techniques
Scripting in the browser
The Ajax approach to browser scripting
Using Flash to display XML
Introduction to server-side XML
Case study : using .NET for an XML application
Case study : using PHP for an XML applicationCôte titre : Fs/19709 Beginning XML with DOM and Ajax : From Novice to Professional [texte imprimé] / Sas Jacobs . - Usa : Apress, 2006 . - 1 vol (432 p.) : ill. ; 24 cm.
ISBN : 978-1-59059-676-0
Langues : Français (fre)
Catégories : Informatique Mots-clés : XML
DOM
AjaxIndex. décimale : 004 Informatique Résumé :
ce livre est tout ce dont vous avez besoin pour aller de l'avant dans le développement XML. Le développeur Web renommé Sas Jacobs présente un guide essentiel pour XML.
Commencer XML avec DOM et Ajax est pratique et complet. Il comprend tout ce dont vous avez besoin pour vous familiariser avec le développement XML rapidement et sans effort.
Jacobs commence par présenter une vue d'ensemble de XML - c'est la syntaxe, les règles, les vocabulaires, et le comment et le pourquoi de la validité. Elle couvre également l'état actuel du support XML dans les navigateurs Web d'aujourd'hui. Ensuite, Jacobs couvre toutes les utilisations essentielles de XML. Vous apprendrez à afficher des données XML à l'aide de CSS et à transformer des données XML à l'aide de XSLT. Vous apprendrez même à propos des scripts XML dynamiques utilisant le DOM XML.
La dernière partie du livre aborde les utilisations avancées du XML côté serveur dans des applications réelles, notamment l'affichage de données XML dans Flash et les applications PHP et ASP.NET basées sur XML. Enfin, Jacobs fournit une introduction parfaite au développement d'Ajax.Note de contenu :
Sommaire
Introduction to XML
Related XML recommendations
Web vocabularies
Client-side XML
Displaying XML using CSS
Introduction to XSLT
Advanced client-side XSLT techniques
Scripting in the browser
The Ajax approach to browser scripting
Using Flash to display XML
Introduction to server-side XML
Case study : using .NET for an XML application
Case study : using PHP for an XML applicationCôte titre : Fs/19709 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/19709 Fs/19709 Livre Bibliothéque des sciences Français Disponible
DisponibleMySQL Database Service Revealed: Running MySQL as a Service in the Oracle Cloud Infrastructure / Bell Charles
Titre : MySQL Database Service Revealed: Running MySQL as a Service in the Oracle Cloud Infrastructure Type de document : texte imprimé Auteurs : Bell Charles, Auteur Editeur : Apress Année de publication : 2022 Importance : 1 vol (514 p.) Format : 24 cm ISBN/ISSN/EAN : 978-1-4842-8944-0 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé :
Access all the information you need to begin using the MySQL Database Service (MDS) in the Oracle Cloud Infrastructure (OCI). MDS is Oracle’s new platform as a service (PAAS) offering for open-source database users. This book covers getting started with an account in OCI, gives a brief overview of OCI services available, and provides a short tutorial on MDS. Reading this book helps you take advantage of the powerful OCI features by building your own MySQL database in the cloud.
Examples in this book center around running MDS in OCI, and include several of the popular use cases as well as advice on how to implement them. In addition, you will learn more about the related MDS OCI features, such as the high availability features currently available. Finally, you will learn how to back up and restore your data as well as how to get your data into and out of the cloud. The skills you learn in this book will help you get started using MDS and letting Oracle do the heavy lifting of managing MDS operations and implementation.Côte titre : Fs/25024 MySQL Database Service Revealed: Running MySQL as a Service in the Oracle Cloud Infrastructure [texte imprimé] / Bell Charles, Auteur . - Usa : Apress, 2022 . - 1 vol (514 p.) ; 24 cm.
ISBN : 978-1-4842-8944-0
Langues : Anglais (eng)
Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé :
Access all the information you need to begin using the MySQL Database Service (MDS) in the Oracle Cloud Infrastructure (OCI). MDS is Oracle’s new platform as a service (PAAS) offering for open-source database users. This book covers getting started with an account in OCI, gives a brief overview of OCI services available, and provides a short tutorial on MDS. Reading this book helps you take advantage of the powerful OCI features by building your own MySQL database in the cloud.
Examples in this book center around running MDS in OCI, and include several of the popular use cases as well as advice on how to implement them. In addition, you will learn more about the related MDS OCI features, such as the high availability features currently available. Finally, you will learn how to back up and restore your data as well as how to get your data into and out of the cloud. The skills you learn in this book will help you get started using MDS and letting Oracle do the heavy lifting of managing MDS operations and implementation.Côte titre : Fs/25024 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/25024 Fs/25024 livre Bibliothéque des sciences Anglais Disponible
Disponible
Titre : Numerical Methods Using Java : for data science, analysis, and engineering Type de document : texte imprimé Auteurs : Haksun Li, Auteur Editeur : Apress Année de publication : 2022 Importance : 1 vol (1186 f.) Présentation : illustrations Format : 26 cm. ISBN/ISSN/EAN : 978-1-4842-6796-7 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé :
Implement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You’ll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes.
Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started.Note de contenu :
Sommaire
1: Introduction to Numerical Methods in Java
2: Linear Algebra
3: Finding Roots of Equations
4: Finding Roots of Systems of Equations
5: Curve Fitting and Interpolation
6: Numerical Differentiation and Integration
7: Ordinary Differential Equations
8: Partial Differential Equations
9: Unconstrained Optimization
10: Constrained Optimization
11: Heuristics
12: Basic Statistics
13: Random Numbers and Simulation
14: Linear Regression
15: Times Series Analysis
ReferencesCôte titre : Fs/25025 Numerical Methods Using Java : for data science, analysis, and engineering [texte imprimé] / Haksun Li, Auteur . - Usa : Apress, 2022 . - 1 vol (1186 f.) : illustrations ; 26 cm.
ISBN : 978-1-4842-6796-7
Langues : Anglais (eng)
Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé :
Implement numerical algorithms in Java using NM Dev, an object-oriented and high-performance programming library for mathematics.You’ll see how it can help you easily create a solution for your complex engineering problem by quickly putting together classes.
Numerical Methods Using Java covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started.Note de contenu :
Sommaire
1: Introduction to Numerical Methods in Java
2: Linear Algebra
3: Finding Roots of Equations
4: Finding Roots of Systems of Equations
5: Curve Fitting and Interpolation
6: Numerical Differentiation and Integration
7: Ordinary Differential Equations
8: Partial Differential Equations
9: Unconstrained Optimization
10: Constrained Optimization
11: Heuristics
12: Basic Statistics
13: Random Numbers and Simulation
14: Linear Regression
15: Times Series Analysis
ReferencesCôte titre : Fs/25025 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/25025 Fs/25025 livre Bibliothéque des sciences Anglais Disponible
Disponible
Titre : Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python Type de document : texte imprimé Auteurs : Pattanayak Santanu, Auteur Editeur : Apress Année de publication : 2023 Importance : 1 vol.(652 p.) Format : 24 cm ISBN/ISSN/EAN : 978-1-4842-8930-3 Langues : Anglais (eng) Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé :
This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.
Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE.
Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications.Côte titre : Fs/25028 Pro Deep Learning with TensorFlow 2.0: A Mathematical Approach to Advanced Artificial Intelligence in Python [texte imprimé] / Pattanayak Santanu, Auteur . - Usa : Apress, 2023 . - 1 vol.(652 p.) ; 24 cm.
ISBN : 978-1-4842-8930-3
Langues : Anglais (eng)
Catégories : Informatique Mots-clés : Informatique Index. décimale : 004 - Informatique Résumé :
This book builds upon the foundations established in its first edition, with updated chapters and the latest code implementations to bring it up to date with Tensorflow 2.0.
Pro Deep Learning with TensorFlow 2.0 begins with the mathematical and core technical foundations of deep learning. Next, you will learn about convolutional neural networks, including new convolutional methods such as dilated convolution, depth-wise separable convolution, and their implementation. You’ll then gain an understanding of natural language processing in advanced network architectures such as transformers and various attention mechanisms relevant to natural language processing and neural networks in general. As you progress through the book, you’ll explore unsupervised learning frameworks that reflect the current state of deep learning methods, such as autoencoders and variational autoencoders. The final chapter covers the advanced topic of generative adversarial networks and their variants, such as cycle consistency GANs and graph neural network techniques such as graph attention networks and GraphSAGE.
Upon completing this book, you will understand the mathematical foundations and concepts of deep learning, and be able to use the prototypes demonstrated to build new deep learning applications.Côte titre : Fs/25028 Exemplaires (1)
Code-barres Cote Support Localisation Section Disponibilité Fs/25028 Fs/25028 livre Bibliothéque des sciences Anglais Disponible
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