Bibliothèque de L'institut de Technologie UAMO BOUIRA
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Ouvrages de la bibliothèque en indexation 006.31 (8)
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(2025)
| title : |
Introduction to Deep Learning |
| Type de document : |
printed text |
| Auteur : |
Mairicio Albrto Ortega Ruiz, Author |
| Editeur : |
Burlington [CANADA] : Tronto Academic press TAP |
| Date de publication : |
2025 |
| ISBN (ou autre code) : |
978-1-77956-299-9 |
| Langue : |
English (eng) |
| Mots clé : |
neurons deep learning application |
| Indexation : |
006.31 |
| Résumé : |
This book is designed to provide a comprehensive introduction to the field of deep learning, covering its foundational principles, techniques, and applications. It covers topics such as neural networks, convolutional networks, recurrent networks, and deep reinforcement learning. The content emphasizes both the theoretical concepts and practical implementations of deep learning models, providing insights into how these models are trained and applied to solve complex problems. Practical examples and hands-on exercises are included to help readers develop a solid understanding of deep learning techniques and their applications in various fields.
« Moins |
Introduction to Deep Learning [printed text] / Mairicio Albrto Ortega Ruiz, Author . - Burlington [CANADA] : Tronto Academic press TAP, 2025. ISBN : 978-1-77956-299-9 Langue : English ( eng)
| Mots clé : |
neurons deep learning application |
| Indexation : |
006.31 |
| Résumé : |
This book is designed to provide a comprehensive introduction to the field of deep learning, covering its foundational principles, techniques, and applications. It covers topics such as neural networks, convolutional networks, recurrent networks, and deep reinforcement learning. The content emphasizes both the theoretical concepts and practical implementations of deep learning models, providing insights into how these models are trained and applied to solve complex problems. Practical examples and hands-on exercises are included to help readers develop a solid understanding of deep learning techniques and their applications in various fields.
« Moins |
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Exemplaires(0)

(2025)
| title : |
Introductio to Machine Learning |
| Type de document : |
printed text |
| Auteur : |
Bechoo Dr .Lal, Author |
| Editeur : |
Burlington [CANADA] : TAP Toronto Academic Press |
| Date de publication : |
2025 |
| ISBN (ou autre code) : |
978-1-77956-300-2 |
| Langue : |
English (eng) |
| Mots clé : |
categorical features data privacy |
| Indexation : |
006.31 |
| Résumé : |
This book is a comprehensive introduction to the principles and techniques of machine learning. It covers key topics such as supervised and unsupervised learning, neural networks, decision trees, support vector machines, and clustering algorithms. The content is structured to guide readers from basic concepts to more advanced topics, ensuring a thorough understanding of both theoretical foundations and practical applications. Real-world examples, case studies, and hands-on exercises are included to illustrate the application of machine learning techniques in various domains such as finance, healthcare, and technology. |
Introductio to Machine Learning [printed text] / Bechoo Dr .Lal, Author . - Burlington [CANADA] : TAP Toronto Academic Press, 2025. ISBN : 978-1-77956-300-2 Langue : English ( eng)
| Mots clé : |
categorical features data privacy |
| Indexation : |
006.31 |
| Résumé : |
This book is a comprehensive introduction to the principles and techniques of machine learning. It covers key topics such as supervised and unsupervised learning, neural networks, decision trees, support vector machines, and clustering algorithms. The content is structured to guide readers from basic concepts to more advanced topics, ensuring a thorough understanding of both theoretical foundations and practical applications. Real-world examples, case studies, and hands-on exercises are included to illustrate the application of machine learning techniques in various domains such as finance, healthcare, and technology. |
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Exemplaires(0)

(2020)
| title : |
Statistical Learning Using Neural Network : A Guide for Statisticians and Data Scientists with Python |
| Type de document : |
printed text |
| Auteur : |
Basilio de Bragança Pereira, Author ; Calyampudi Radhakrishna Rao (1920-2023), Author |
| Editeur : |
Boca Raton : CRC Press/Taylor & Francis Group |
| Date de publication : |
2020 |
| ISBN (ou autre code) : |
978-1-03-233593-3 |
| Langue : |
English (eng) |
| Mots clé : |
regression neural network models some common neural network model |
| Indexation : |
006.31 |
| Résumé : |
Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students.
Key Features:
Discusses applications in several research areas
Covers a wide range of widely used statistical methodologies
Includes Python code examples
Gives numerous neural network models
This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results.
This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks. |
Statistical Learning Using Neural Network : A Guide for Statisticians and Data Scientists with Python [printed text] / Basilio de Bragança Pereira, Author ; Calyampudi Radhakrishna Rao (1920-2023), Author . - Boca Raton : CRC Press/Taylor & Francis Group, 2020. ISBN : 978-1-03-233593-3 Langue : English ( eng)
| Mots clé : |
regression neural network models some common neural network model |
| Indexation : |
006.31 |
| Résumé : |
Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks starting from the basics and increasingly demanding more effort from readers, who can learn the theory and its applications in statistical methods with concrete Python code examples. It presents a wide range of widely used statistical methodologies, applied in several research areas with Python code examples, which are available online. It is suitable for scientists and developers as well as graduate students.
Key Features:
Discusses applications in several research areas
Covers a wide range of widely used statistical methodologies
Includes Python code examples
Gives numerous neural network models
This book covers fundamental concepts on Neural Networks including Multivariate Statistics Neural Networks, Regression Neural Network Models, Survival Analysis Networks, Time Series Forecasting Networks, Control Chart Networks, and Statistical Inference Results.
This book is suitable for both teaching and research. It introduces neural networks and is a guide for outsiders of academia working in data mining and artificial intelligence (AI). This book brings together data analysis from statistics to computer science using neural networks. |
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Exemplaires(0)

(1998)
| title : |
Abrege de chimie industrielle |
| Type de document : |
printed text |
| Auteur : |
Pierre Laszlo |
| Editeur : |
Paris : Ellipses |
| Date de publication : |
1998 |
| Nombre de pages : |
159 p. |
| Ill. : |
ill., couv. ill. en coul. |
| Dimensions : |
19 cm |
| ISBN (ou autre code) : |
978-2-7298-9853-3 |
| Note général : |
Bibliogr. p. 156. Index |
| Langue : |
French (fre) |
| Indexation : |
006.31 |
| Résumé : |
Cet ouvrage est issu d'un cours enseigné aux élèves de l'Ecole polytechnique depuis le début de la décennie Pierre Laszlo, fort d'une longue expérience de consultant industriel, montre dans ce livre les lignes de force qui structurent la chimie d'aujourd'hui. Celle-ci subit des mutations brutales : basculement vers la région Asie-Pacifique ; spécialisation dans les seuls secteurs d'excellence ; innovation de formulation, plutôt qu'innovation de produit ou de procédé. Cet Abrégé, focalisé sur l'essentiel (principaux produits, procédés majeurs, problèmes pendants), au format clair et aéré, d'une conception modulaire, intègre une documentation à jour et les derniers acquis, dans ce secteur vital pour l'économie.
Sommaire
Caractéristiques générales
La pétrochimie
Polymères et élastomères
Quelques grands procédés
La filière chloralcali
La chimie fine
Génie chimique
Atteintes à l'environnement |
Abrege de chimie industrielle [printed text] / Pierre Laszlo . - Paris : Ellipses, 1998 . - 159 p. : ill., couv. ill. en coul. ; 19 cm. ISBN : 978-2-7298-9853-3 Bibliogr. p. 156. Index Langue : French ( fre)
| Indexation : |
006.31 |
| Résumé : |
Cet ouvrage est issu d'un cours enseigné aux élèves de l'Ecole polytechnique depuis le début de la décennie Pierre Laszlo, fort d'une longue expérience de consultant industriel, montre dans ce livre les lignes de force qui structurent la chimie d'aujourd'hui. Celle-ci subit des mutations brutales : basculement vers la région Asie-Pacifique ; spécialisation dans les seuls secteurs d'excellence ; innovation de formulation, plutôt qu'innovation de produit ou de procédé. Cet Abrégé, focalisé sur l'essentiel (principaux produits, procédés majeurs, problèmes pendants), au format clair et aéré, d'une conception modulaire, intègre une documentation à jour et les derniers acquis, dans ce secteur vital pour l'économie.
Sommaire
Caractéristiques générales
La pétrochimie
Polymères et élastomères
Quelques grands procédés
La filière chloralcali
La chimie fine
Génie chimique
Atteintes à l'environnement |
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Exemplaires(0)

(1966)

(2004)
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(2017)
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(2021)
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