Applied Deep Learning Tools, Techniques, and Implementation /
Fergus, Paul.
Applied Deep Learning Tools, Techniques, and Implementation / [electronic resource] : by Paul Fergus, Carl Chalmers. - 1st ed. 2022. - XXVII, 341 p. 1 illus. online resource. - Computational Intelligence Methods and Applications, 2510-1773 . - Computational Intelligence Methods and Applications, .
Part 1 Introduction and Overview -- Introduction -- Part 2 Foundations of Mashine Learning -- Fundamentals of Machine Learning -- Supervised Learning -- Un-Supervised Learning -- Performance Evaluation Metrics -- Part 3 Deep Learning Concepts and Techniques -- Introduction to Deep Learning -- Image Classification and Object Detection -- Deep Learning Techniques for Time Series Modelling -- Natural Language Processing -- Deep Generative Models -- Deep Reinforcement Learning -- Part 4 Enterprise Machine Learning -- Accelerated Machine Learning -- Deploying and Hosting Machine Learning Models -- Enterprise Machine Learning Serving. .
This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks. The book covers all of the key conceptual aspects of the field and provides a foundation for all interested parties to develop their own artificial intelligence applications.
9783031044205
10.1007/978-3-031-04420-5 doi
Artificial intelligence.
Artificial Intelligence.
Q334-342 TA347.A78
006.3
Applied Deep Learning Tools, Techniques, and Implementation / [electronic resource] : by Paul Fergus, Carl Chalmers. - 1st ed. 2022. - XXVII, 341 p. 1 illus. online resource. - Computational Intelligence Methods and Applications, 2510-1773 . - Computational Intelligence Methods and Applications, .
Part 1 Introduction and Overview -- Introduction -- Part 2 Foundations of Mashine Learning -- Fundamentals of Machine Learning -- Supervised Learning -- Un-Supervised Learning -- Performance Evaluation Metrics -- Part 3 Deep Learning Concepts and Techniques -- Introduction to Deep Learning -- Image Classification and Object Detection -- Deep Learning Techniques for Time Series Modelling -- Natural Language Processing -- Deep Generative Models -- Deep Reinforcement Learning -- Part 4 Enterprise Machine Learning -- Accelerated Machine Learning -- Deploying and Hosting Machine Learning Models -- Enterprise Machine Learning Serving. .
This book focuses on the applied aspects of artificial intelligence using enterprise frameworks and technologies. The book is applied in nature and will equip the reader with the necessary skills and understanding for delivering enterprise ML technologies. It will be valuable for undergraduate and postgraduate students in subjects such as artificial intelligence and data science, and also for industrial practitioners engaged with data analytics and machine learning tasks. The book covers all of the key conceptual aspects of the field and provides a foundation for all interested parties to develop their own artificial intelligence applications.
9783031044205
10.1007/978-3-031-04420-5 doi
Artificial intelligence.
Artificial Intelligence.
Q334-342 TA347.A78
006.3