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Applied Deep Learning: Tools, Techniques, and Implementation, is aimed at students, academics and industry practitioners to provide them with a conceptual overview of the field. Students can use this book to supplement undergraduate, postgraduate and doctoral studies. Academics who are new to the area can utilise this book to gain a broad understanding of Artificial Intelligence while seasoned academics can use the book as a point of reference. In industry, managers will find this book useful for gaining an understanding of Artificial Intelligence and where it could be integrated into existing business processes. For those primally focused on development and implementation, the book along with its references provides a strong foundation for anyone moving into Artificial Intelligence development. The book discusses key frameworks such as TensorFlow, Dask, RAPIDS, Docker and Kubernetes. What makes this book accessible to a broad range of readers is the conscious decision to minimise both mathematical and programming notation and focus more on the core and practical concepts of Artificial Intelligence and its deployment. Once the reader has a good grasp of the concepts, understanding the theoretical principles becomes much easier.
Preface.
Acknowledgements.
Introduction and Overview
Introduction.
Foundations of Machine Learning
Fundamentals of Machine Learning.
Supervised Learning.
Un-Supervised Learning.
Performance Evaluation Metrics.
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.
Enterprise Machine Learning
Accelerated Machine Learning.
Deploying and Hosting Machine Learning Models.
Enterprise Machine Learning Serving