Artificial intelligence-based smart power systems (2023)

Public ISBD Titre : Artificial intelligence-based smart power systems Type de document : texte imprimé Auteurs : Sanjeevikumar Padmanaban, Éditeur scientifique ; Sivaraman Palanisamy, Éditeur scientifique ; Sharmeela Chenniappan, Éditeur scientifique ; Jens Bo Holm-Nielsen, Éditeur scientifique Editeur : Piscataway, NJ : IEEE Press Année de publication : 2023 Autre Editeur : Hoboken, NJ : Wiley Importance : XXII, 378 p. Présentation : ill. Format : 26 cm ISBN/ISSN/EAN : 978-1-119-89396-7 Note générale : Ref. Bibliogr. en fin de chapitres. - Index Langues : Anglais (eng) Mots-clés : Smart power grids Artificial intelligence Réseaux électriques intelligents Intelligence artificielle Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies Artificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years. To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies. Note de contenu : Summary : 1. Introduction to Smart Power Systems 2. Modeling and Analysis of Smart Power System 3. Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications 4. Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 5. Energy Storage Systems for Smart Power Systems 6. Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage 7. Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator 8. A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 9. Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability 10. Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 11. Deep Reinforcement Learning and Energy Price Prediction 12. Power Quality Conditioners in Smart Power System 13. The Role of Internet of Things in Smart Homes 14. Electric Vehicles and IoT in Smart Cities 15. Modeling and Simulation of Smart Power Systems Using HIL 16. Distribution Phasor Measurement Units (PMUs) in Smart Power Systems 17. Blockchain Technologies for Smart Power Systems 18. Power and Energy Management in Smart Power Systems Artificial intelligence-based smart power systems [texte imprimé] / Sanjeevikumar Padmanaban, Éditeur scientifique ; Sivaraman Palanisamy, Éditeur scientifique ; Sharmeela Chenniappan, Éditeur scientifique ; Jens Bo Holm-Nielsen, Éditeur scientifique . - Piscataway, NJ : IEEE Press : Hoboken, NJ : Wiley, 2023 . - XXII, 378 p. : ill. ; 26 cm. ISBN : 978-1-119-89396-7 Ref. Bibliogr. en fin de chapitres. - Index Langues : Anglais (eng) Mots-clés : Smart power grids Artificial intelligence Réseaux électriques intelligents Intelligence artificielle Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : Authoritative resource describing artificial intelligence and advanced technologies in smart power systems with simulation examples and case studies Artificial Intelligence-based Smart Power Systems presents advanced technologies used in various aspects of smart power systems, especially grid-connected and industrial evolution. It covers many new topics such as distribution phasor measurement units, blockchain technologies for smart power systems, the application of deep learning and reinforced learning, and artificial intelligence techniques. The text also explores the potential consequences of artificial intelligence and advanced technologies in smart power systems in the forthcoming years. To enhance and reinforce learning, the editors include many learning resources throughout the text, including MATLAB, practical examples, and case studies. Note de contenu : Summary : 1. Introduction to Smart Power Systems 2. Modeling and Analysis of Smart Power System 3. Multilevel Cascaded Boost Converter Fed Multilevel Inverter for Renewable Energy Applications 4. Recent Advancements in Power Electronics for Modern Power Systems-Comprehensive Review on DC-Link Capacitors Concerning Power Density Maximization in Power Converters 5. Energy Storage Systems for Smart Power Systems 6. Real-Time Implementation and Performance Analysis of Supercapacitor for Energy Storage 7. Adaptive Fuzzy Logic Controller for MPPT Control in PMSG Wind Turbine Generator 8. A Novel Nearest Neighbor Searching-Based Fault Distance Location Method for HVDC Transmission Lines 9. Comparative Analysis of Machine Learning Approaches in Enhancing Power System Stability 10. Augmentation of PV-Wind Hybrid Technology with Adroit Neural Network, ANFIS, and PI Controllers Indeed Precocious DVR System 11. Deep Reinforcement Learning and Energy Price Prediction 12. Power Quality Conditioners in Smart Power System 13. The Role of Internet of Things in Smart Homes 14. Electric Vehicles and IoT in Smart Cities 15. Modeling and Simulation of Smart Power Systems Using HIL 16. Distribution Phasor Measurement Units (PMUs) in Smart Power Systems 17. Blockchain Technologies for Smart Power Systems 18. Power and Energy Management in Smart Power Systems

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
061222	004.89 ART	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	En bon état
061223	004.89 ART	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Consultation sur place

Artificial intelligence in industry 4.0 and 5G technology (2022)

Public ISBD Titre : Artificial intelligence in industry 4.0 and 5G technology Type de document : texte imprimé Auteurs : Pandian Vasant, Éditeur scientifique ; Elias Munapo, Éditeur scientifique ; J. Joshua Thomas (1973-....), Éditeur scientifique ; Gerhard-Wilhelm Weber, Éditeur scientifique Editeur : Hoboken, NJ : John Wiley et Sons, Inc. Année de publication : 2022 Importance : XXX, 321 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-119-79876-7 Note générale : Ref. Bibliogr. en fin de chapitres. - Index Langues : Anglais (eng) Mots-clés : Artificial intelligence -- Industrial applications Industry 4.0 5G mobile communication systems Intelligence artificielle -- Applications industrielles Industrie Communications mobiles 5G Index. décimale : 004.896 Intelligence artificielle dans les systèmes industriels Résumé : Artificial Intelligence in Industry 4.0 and 5G Technology Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise. Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT). Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks. Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor. Provides guidance on implementing metaheuristics in different applications and hybrid technological systems. Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement. Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches. Note de contenu : Summary : 1. Dynamic Key-based Biometric End-User Authentication Proposal for IoT in Industry 2. Decision Support Methodology for Scheduling Orders in Additive Manufacturing 3. Significance of Consuming 5G-Built Artificial Intelligence in Smart Cities 4. Neural Network Approach to Segmentation of Economic Infrastructure Objects on High-Resolution Satellite Images 5. The Impact of Data Security on the Internet of Things 6. Sustainable Renewable Energy and Waste Management on Weathering Corporate Pollution 7. Adam Adaptive Optimization Method for Neural Network Models Regression in Image Recognition Tasks 8. Application of Integer Programming in Allocating Energy Resources in Rural Africa 9. Feasibility of Drones as the Next Step in Innovative Solution for Emerging Society 10. Designing a Distribution Network for a Soda Company: Formulation and Efficient Solution Procedure 11. Machine Learning and MCDM Approach to Characterize Student Attrition in Higher Education 12. A Concise Review on Recent Optimization and Deep Learning Applications in Blockchain Technology 13. Inventory Routing Problem with Fuzzy Demand and Deliveries with Priority 14. Comparison of Defuzzification Methods for Project Selection 15. Re-Identification-Based Models for Multiple Object Tracking Artificial intelligence in industry 4.0 and 5G technology [texte imprimé] / Pandian Vasant, Éditeur scientifique ; Elias Munapo, Éditeur scientifique ; J. Joshua Thomas (1973-....), Éditeur scientifique ; Gerhard-Wilhelm Weber, Éditeur scientifique . - Hoboken, NJ : John Wiley et Sons, Inc., 2022 . - XXX, 321 p. : ill. ; 24 cm. ISBN : 978-1-119-79876-7 Ref. Bibliogr. en fin de chapitres. - Index Langues : Anglais (eng) Mots-clés : Artificial intelligence -- Industrial applications Industry 4.0 5G mobile communication systems Intelligence artificielle -- Applications industrielles Industrie Communications mobiles 5G Index. décimale : 004.896 Intelligence artificielle dans les systèmes industriels Résumé : Artificial Intelligence in Industry 4.0 and 5G Technology Explores innovative and value-added solutions for application problems in the commercial, business, and industry sectors As the pace of Artificial Intelligence (AI) technology innovation continues to accelerate, identifying the appropriate AI capabilities to embed in key decision processes has never been more critical to establishing competitive advantage. New and emerging analytics tools and technologies can be configured to optimize business value, change how an organization gains insights, and significantly improve the decision-making process across the enterprise. Artificial Intelligence in Industry 4.0 and 5G Technology helps readers solve real-world technological engineering optimization problems using evolutionary and swarm intelligence, mathematical programming, multi-objective optimization, and other cutting-edge intelligent optimization methods. Contributions from leading experts in the field present original research on both the theoretical and practical aspects of implementing new AI techniques in a variety of sectors, including Big Data analytics, smart manufacturing, renewable energy, smart cities, robotics, and the Internet of Things (IoT). Presents detailed information on meta-heuristic applications with a focus on technology and engineering sectors such as smart manufacturing, smart production, innovative cities, and 5G networks. Offers insights into the use of metaheuristic strategies to solve optimization problems in business, economics, finance, and industry where uncertainty is a factor. Provides guidance on implementing metaheuristics in different applications and hybrid technological systems. Describes various AI approaches utilizing hybrid meta-heuristics optimization algorithms, including meta-search engines for innovative research and hyper-heuristics algorithms for performance measurement. Artificial Intelligence in Industry 4.0 and 5G Technology is a valuable resource for IT specialists, industry professionals, managers and executives, researchers, scientists, engineers, and advanced students an up-to-date reference to innovative computing, uncertainty management, and optimization approaches. Note de contenu : Summary : 1. Dynamic Key-based Biometric End-User Authentication Proposal for IoT in Industry 2. Decision Support Methodology for Scheduling Orders in Additive Manufacturing 3. Significance of Consuming 5G-Built Artificial Intelligence in Smart Cities 4. Neural Network Approach to Segmentation of Economic Infrastructure Objects on High-Resolution Satellite Images 5. The Impact of Data Security on the Internet of Things 6. Sustainable Renewable Energy and Waste Management on Weathering Corporate Pollution 7. Adam Adaptive Optimization Method for Neural Network Models Regression in Image Recognition Tasks 8. Application of Integer Programming in Allocating Energy Resources in Rural Africa 9. Feasibility of Drones as the Next Step in Innovative Solution for Emerging Society 10. Designing a Distribution Network for a Soda Company: Formulation and Efficient Solution Procedure 11. Machine Learning and MCDM Approach to Characterize Student Attrition in Higher Education 12. A Concise Review on Recent Optimization and Deep Learning Applications in Blockchain Technology 13. Inventory Routing Problem with Fuzzy Demand and Deliveries with Priority 14. Comparison of Defuzzification Methods for Project Selection 15. Re-Identification-Based Models for Multiple Object Tracking

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
061220	004.896 ART	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	En bon état
061221	004.896 ART	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Consultation sur place

Convergence (2022)

Public ISBD Titre : Convergence : artificial intelligence and quantum computing Type de document : texte imprimé Auteurs : Greg Viggiano, Éditeur scientifique ; David Brin (1950-....), Préfacier, etc. Editeur : Hoboken, NJ : Wiley Année de publication : 2022 Importance : XLVIII, 272 p. Présentation : ill. Format : 23 cm ISBN/ISSN/EAN : 978-1-394-17410-2 Note générale : La couverture indique en plus "Social, economic, and policy impacts". - Notes bibliogr. p. 251-258. - Index Langues : Anglais (eng) Mots-clés : Artificial intelligence -- Social aspects Quantum computing -- Social aspects Artificial intelligence -- Economic aspects Quantum computing -- Economic aspects Intelligence artificielle -- Aspect social Informatique quantique -- Aspect social Intelligence artificielle -- Aspect économique Informatique quantique -- Aspect économique Index. décimale : 004.8 Intelligence artificielle Résumé : "Prepare for the coming convergence of AI and quantum computing. A collection of essays from 20 renowned, international authors working in industry, academia, and government, Convergence: Artificial Intelligence and Quantum Computing explains the impending convergence of artificial intelligence and quantum computing. A diversity of viewpoints is presented, each offering their view of this coming watershed event. In the book, you'll discover that we're on the cusp of seeing the stuff of science fiction become reality, with huge implications for ripping up the existing social fabric, global economy, and current geopolitical order. Along with an incisive foreword by Hugo- and Nebula-award winning author David Brin, you'll also find: Explorations of the increasing pace of technological development; Explanations of why seemingly unusual and surprising breakthroughs might be just around the corner; Maps to navigate the potential minefields that await us as AI and quantum computing come together A fascinating and thought-provoking compilation of insights from some of the leading technological voices in the world, Convergence convincingly argues that we should prepare for a world in which very little will remain the same and shows us how to get ready."-- Provided by publisher. Note de contenu : Summary : Part I. Policy and Regulatory Impacts Part II. Economic Impacts Part III. Social Impacts Convergence : artificial intelligence and quantum computing [texte imprimé] / Greg Viggiano, Éditeur scientifique ; David Brin (1950-....), Préfacier, etc. . - Hoboken, NJ : Wiley, 2022 . - XLVIII, 272 p. : ill. ; 23 cm. ISBN : 978-1-394-17410-2 La couverture indique en plus "Social, economic, and policy impacts". - Notes bibliogr. p. 251-258. - Index Langues : Anglais (eng) Mots-clés : Artificial intelligence -- Social aspects Quantum computing -- Social aspects Artificial intelligence -- Economic aspects Quantum computing -- Economic aspects Intelligence artificielle -- Aspect social Informatique quantique -- Aspect social Intelligence artificielle -- Aspect économique Informatique quantique -- Aspect économique Index. décimale : 004.8 Intelligence artificielle Résumé : "Prepare for the coming convergence of AI and quantum computing. A collection of essays from 20 renowned, international authors working in industry, academia, and government, Convergence: Artificial Intelligence and Quantum Computing explains the impending convergence of artificial intelligence and quantum computing. A diversity of viewpoints is presented, each offering their view of this coming watershed event. In the book, you'll discover that we're on the cusp of seeing the stuff of science fiction become reality, with huge implications for ripping up the existing social fabric, global economy, and current geopolitical order. Along with an incisive foreword by Hugo- and Nebula-award winning author David Brin, you'll also find: Explorations of the increasing pace of technological development; Explanations of why seemingly unusual and surprising breakthroughs might be just around the corner; Maps to navigate the potential minefields that await us as AI and quantum computing come together A fascinating and thought-provoking compilation of insights from some of the leading technological voices in the world, Convergence convincingly argues that we should prepare for a world in which very little will remain the same and shows us how to get ready."-- Provided by publisher. Note de contenu : Summary : Part I. Policy and Regulatory Impacts Part II. Economic Impacts Part III. Social Impacts

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
061246	004.8 CON	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	En bon état
061247	004.8 CON	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Consultation sur place

Engineering intelligent systems / Barclay R. Brown (2023)

Public ISBD Titre : Engineering intelligent systems : systems engineering and design with artificial intelligence, visual modeling, and systems thinking Type de document : texte imprimé Auteurs : Barclay R. Brown, Auteur Editeur : Hoboken, NJ : John Wiley et Sons, Inc. Année de publication : 2023 Importance : XV, 365 p. Présentation : ill. Format : 24 cm ISBN/ISSN/EAN : 978-1-119-66559-5 Note générale : Ref. Bibliogr. - Index Langues : Anglais (eng) Mots-clés : Systems engineering Systems engineering -- Design Artificial intelligence Systems engineering -- Simulation methods Ingénierie des systèmes Intelligence artificielle Ingénierie des systèmes -- Méthodes de simulation Index. décimale : 004.8 Intelligence artificielle Résumé : Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown's 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithms and devices, this book puts forth the innovative idea of transforming the systems in our lives, our societies, and our businesses into intelligent systems. At its heart, this book is about how to combine systems engineering and systems thinking with the newest technologies to design increasingly intelligent systems. Engineering Intelligent Systems readers will also find: An introduction to the fields of artificial intelligence with machine learning, model-based systems engineering (MBSE), and systems thinking--the key disciplines for making systems smarter An example of how to build a deep neural network in a spreadsheet, with no code or specialized mathematics required An approach to the visual representation of systems, using techniques from moviemaking, storytelling, visual systems design, and model-based systems engineering An analysis of the potential ability of computers to think, understand and become conscious and its implications for artificial intelligence Tools to allow for easier collaboration and communication among developers and engineers, allowing for better understanding between stakeholders, and creating a faster development cycle A systems thinking approach to people systems--systems that consist only of people and which form the basis for our organizations, communities and society Engineering Intelligent Systems offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers. Note de contenu : Summary : Part I: systems and artificial intelligence 1. Artificial intelligence, science fiction, and fear 2. We live in a world of systems 3. The intelligence in the system: how artificial intelligence 4. Intelligent systems and the people they lov Part II: systems engineering for intelligent systems 5. Designing systems by drawing pictures and telling 6. Use cases: the superpower of systems engineering 7. Picturing systems with model based systems 8. A time for timeboxes and the use of usage processes Part III: systems thinking for intelligent systems 9. Solving hard problems with systems thinking 10. People systems: a new way to understand the world Engineering intelligent systems : systems engineering and design with artificial intelligence, visual modeling, and systems thinking [texte imprimé] / Barclay R. Brown, Auteur . - Hoboken, NJ : John Wiley et Sons, Inc., 2023 . - XV, 365 p. : ill. ; 24 cm. ISBN : 978-1-119-66559-5 Ref. Bibliogr. - Index Langues : Anglais (eng) Mots-clés : Systems engineering Systems engineering -- Design Artificial intelligence Systems engineering -- Simulation methods Ingénierie des systèmes Intelligence artificielle Ingénierie des systèmes -- Méthodes de simulation Index. décimale : 004.8 Intelligence artificielle Résumé : Engineering Intelligent Systems Exploring the three key disciplines of intelligent systems As artificial intelligence (AI) and machine learning technology continue to develop and find new applications, advances in this field have generally been focused on the development of isolated software data analysis systems or of control systems for robots and other devices. By applying model-based systems engineering to AI, however, engineers can design complex systems that rely on AI-based components, resulting in larger, more complex intelligent systems that successfully integrate humans and AI. Engineering Intelligent Systems relies on Dr. Barclay R. Brown's 25 years of experience in software and systems engineering to propose an integrated perspective to the challenges and opportunities in the use of artificial intelligence to create better technological and business systems. While most recent research on the topic has focused on adapting and improving algorithms and devices, this book puts forth the innovative idea of transforming the systems in our lives, our societies, and our businesses into intelligent systems. At its heart, this book is about how to combine systems engineering and systems thinking with the newest technologies to design increasingly intelligent systems. Engineering Intelligent Systems readers will also find: An introduction to the fields of artificial intelligence with machine learning, model-based systems engineering (MBSE), and systems thinking--the key disciplines for making systems smarter An example of how to build a deep neural network in a spreadsheet, with no code or specialized mathematics required An approach to the visual representation of systems, using techniques from moviemaking, storytelling, visual systems design, and model-based systems engineering An analysis of the potential ability of computers to think, understand and become conscious and its implications for artificial intelligence Tools to allow for easier collaboration and communication among developers and engineers, allowing for better understanding between stakeholders, and creating a faster development cycle A systems thinking approach to people systems--systems that consist only of people and which form the basis for our organizations, communities and society Engineering Intelligent Systems offers an intriguing new approach to making systems more intelligent using artificial intelligence, machine learning, systems thinking, and system modeling and therefore will be of interest to all engineers and business professionals, particularly systems engineers. Note de contenu : Summary : Part I: systems and artificial intelligence 1. Artificial intelligence, science fiction, and fear 2. We live in a world of systems 3. The intelligence in the system: how artificial intelligence 4. Intelligent systems and the people they lov Part II: systems engineering for intelligent systems 5. Designing systems by drawing pictures and telling 6. Use cases: the superpower of systems engineering 7. Picturing systems with model based systems 8. A time for timeboxes and the use of usage processes Part III: systems thinking for intelligent systems 9. Solving hard problems with systems thinking 10. People systems: a new way to understand the world

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
061253	004.8 BRO	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Consultation sur place
061252	004.8 BRO	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Consultation sur place

Machine learning on geographical data using Python / Joos Korstanje (2023)

Public ISBD Titre : Machine learning on geographical data using Python : introduction into geodata with applications and use cases Type de document : texte imprimé Auteurs : Joos Korstanje, Auteur Editeur : New York : Apress Année de publication : 2023 Importance : XV, 312 p. Présentation : ill. Format : 25 cm ISBN/ISSN/EAN : 978-1-4842-8286-1 Note générale : Index Langues : Anglais (eng) Mots-clés : Geodatabases Machine learning Python (Computer program language) Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. Note de contenu : Summary : Part I: General introduction Chapter 1: Introduction to Geodata Chapter 2: Coordinate Systems and Projections Chapter 3: Geodata Data Types Chapter 4: Creating Maps Part II: GIS operations Chapter 5: Clipping and Intersecting Chapter 6: Buffering Chapter 7: Merge and Dissolve Chapter 8: Erase Part III: Machine Learning and mathematics Chapter 9: Interpolation Chapter 10: Classification Chapter 11: Regression Chapter 12: Clustering Chapter 13: Conclusion Machine learning on geographical data using Python : introduction into geodata with applications and use cases [texte imprimé] / Joos Korstanje, Auteur . - New York : Apress, 2023 . - XV, 312 p. : ill. ; 25 cm. ISBN : 978-1-4842-8286-1 Index Langues : Anglais (eng) Mots-clés : Geodatabases Machine learning Python (Computer program language) Index. décimale : 004.89 Systèmes d'application d'intelligence artificielle. Systèmes basés sur la connaissance intelligente. Résumé : Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. Note de contenu : Summary : Part I: General introduction Chapter 1: Introduction to Geodata Chapter 2: Coordinate Systems and Projections Chapter 3: Geodata Data Types Chapter 4: Creating Maps Part II: GIS operations Chapter 5: Clipping and Intersecting Chapter 6: Buffering Chapter 7: Merge and Dissolve Chapter 8: Erase Part III: Machine Learning and mathematics Chapter 9: Interpolation Chapter 10: Classification Chapter 11: Regression Chapter 12: Clustering Chapter 13: Conclusion

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
061339	004.89 KOR	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Consultation sur place
061340	004.89 KOR	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Consultation sur place

TensorFlow pour le deep learning / Bharath Ramsundar (2018)

Public ISBD Titre : TensorFlow pour le deep learning : de la régression linéaire à l'apprentissage par renforcement Type de document : texte imprimé Auteurs : Bharath Ramsundar, Auteur ; Reza Bosagh Zadeh, Auteur ; Daniel Rougé (1952-2020 ; mathématicien), Traducteur Editeur : Paris : First Interactive Année de publication : 2018 Importance : XII, 245 p. Présentation : ill. Format : 23 cm ISBN/ISSN/EAN : 978-2-412-04116-1 Note générale : Sur la page de titre et la couverture l'éditeur (O'Reilly) de l'édition originale. - Index Langues : Français (fre) Langues originales : Anglais (eng) Mots-clés : TensorFlow (logiciel) Apprentissage profond Apprentissage automatique Index. décimale : 004.8 Intelligence artificielle Résumé : "Apprenez à résoudre des problèmes d'apprentissage automatique (même difficiles !) avec TensorFlow, la nouvelle bibliothèque logicielle révolutionnaire de Google pour le deep learning. Si vous avez une formation de base en algèbre linéaire et en calcul, ce livre pratique vous introduit dans les arcanes des principes fondamentaux de l'apprentissage automatique en vous montrant comment concevoir des systèmes capables de détecter des objets dans des images, de comprendre du texte et de prédire les propriétés de médicaments potentiels." (4e de couverture) Note de contenu : Au sommaires : 1. Introduction au deep learning 2. Introduction aux primitives de tensorFlow 3. Régression linéaires et logistique avec TensorFlow 4. Réseaux profonds entièrement connectés 5. Optimiser les hyperparamètres 6. Réseaux de neurones convolutifs 7. Réseaux de neurones récurrents 8. Apprentissage par renforcement 9. Entraîner de grands réseaux profonds 10. L'avenir du deep learning TensorFlow pour le deep learning : de la régression linéaire à l'apprentissage par renforcement [texte imprimé] / Bharath Ramsundar, Auteur ; Reza Bosagh Zadeh, Auteur ; Daniel Rougé (1952-2020 ; mathématicien), Traducteur . - Paris : First Interactive, 2018 . - XII, 245 p. : ill. ; 23 cm. ISBN : 978-2-412-04116-1 Sur la page de titre et la couverture l'éditeur (O'Reilly) de l'édition originale. - Index Langues : Français (fre) Langues originales : Anglais (eng) Mots-clés : TensorFlow (logiciel) Apprentissage profond Apprentissage automatique Index. décimale : 004.8 Intelligence artificielle Résumé : "Apprenez à résoudre des problèmes d'apprentissage automatique (même difficiles !) avec TensorFlow, la nouvelle bibliothèque logicielle révolutionnaire de Google pour le deep learning. Si vous avez une formation de base en algèbre linéaire et en calcul, ce livre pratique vous introduit dans les arcanes des principes fondamentaux de l'apprentissage automatique en vous montrant comment concevoir des systèmes capables de détecter des objets dans des images, de comprendre du texte et de prédire les propriétés de médicaments potentiels." (4e de couverture) Note de contenu : Au sommaires : 1. Introduction au deep learning 2. Introduction aux primitives de tensorFlow 3. Régression linéaires et logistique avec TensorFlow 4. Réseaux profonds entièrement connectés 5. Optimiser les hyperparamètres 6. Réseaux de neurones convolutifs 7. Réseaux de neurones récurrents 8. Apprentissage par renforcement 9. Entraîner de grands réseaux profonds 10. L'avenir du deep learning

Réservation

Réserver ce document

Exemplaires (2)

Code-barres	Cote	Support	Localisation	Section	Disponibilité	Etat_Exemplaire
061439	004.8 RAM	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	En bon état
061440	004.8 RAM	Papier	Bibliothèque Centrale	Data sciences_Intelligence artificielle	Disponible	Consultation sur place