Preface
In recent years, Deep Learning has made unprecedented success stories in difficult problems in vision, speech, natural language processing and understanding, and all other areas with abundance of data. The interest in this field from companies, universities, governments, and research organizations has accelerated the advances in the field. This book covers select important topics in Deep Learning with three new chapters, Object Detection, Semantic Segmentation, and Unsupervised Learning using Mutual Information. The advanced theories are explained by giving a background of the principles, digging into the intuition behind the concepts, implementing the equations and algorithms using Keras, and examining the results.
Artificial Intelligence (AI), as it stands today, is still far from being a well-understood field. Deep Learning (DL), as a sub field of AI, is in the same position. While it is far from being a mature field, many real-world applications such as vision-based detection and recognition, autonomous navigation, product recommendation, speech recognition and synthesis, energy conservation, drug discovery, finance, and marketing are already using DL algorithms. Many more applications will be discovered and built. The aim of this book is to explain advanced concepts, give sample implementations, and let the readers as experts in their field identify the target applications.
A field that is not completely mature is a double-edged sword. On one edge, it offers a lot of opportunities for discovery and exploitation. There are many unsolved problems in deep learning. This translates into opportunities to be the first to market – be that in product development, publication, or recognition. The other edge is it would be difficult to trust a not-fully-understood field in a mission-critical environment. We can safely say that if asked, very few machine learning engineers will ride an auto-pilot plane controlled by a deep learning system. There is a lot of work to be done to gain this level of trust. The advanced concepts that are discussed in this book have a high chance of playing a major role as the foundation in gaining this level of trust.
No DL book will be able to completely cover the whole field. This book is not an exception. Given time and space, we could have touched interesting areas like natural language processing and understanding, speech synthesis, automated machine learning (AutoML), graph neural networks (GNNs), Bayesian deep learning, and many others. However, this book believes in choosing and explaining select areas so that readers can take up other fields that are not covered.
As the reader who is about to embark upon reading this book, keep in mind that you chose an area that is exciting and can have a huge impact on society. We are fortunate to have a job that we look forward to working on as we wake up in the morning.
