This book is a detailed reference guide on Deep Learning and its applications. It aims to provide a basic understanding of Deep Learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by Computer Science academics and researchers. By the end of the book, the reader will become familiar with different Deep Learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. Machine Learning proved its usefulness in many applications in the domain of Image Processing and Computer Vision, Medical Imaging, Satellite imaging, Remote Sensing, Surveillance, etc ., over the past decade. At the same time, Machine Learning methods themselves have evolved, particularly Deep Learning methods that have demonstrated significant performance over traditional Machine Learning algorithms.