Applied Deep Learning

Tools, Techniques, and Implementation
1. Auflage 2022
ISBN: 978-3-031-04422-9
Verlag: Springer International Publishing

Buch, Englisch, 341 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 563 g

Reihe: Computational Intelligence Methods and Applications

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.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Chalmers, Carl

Fergus, Paul

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

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.

Über Autor(innen)

Prof. Paul Fergus is a Professor in Machine Learning and Dr. Carl Chambers is a Senior Lecturer in the Dept. of Computer Science of Liverpool John Moores University. Their teaching responsibilities include Machine Learning and Data Science. Their research interest includes Applied Machine Learning, Computer Vision, Signal Processing, and Pattern Recognition.