DEEP LEARNING

This book offers a thorough exploration of deep learning, providing both foundational knowledge and practical insights that are essential for anyone interested in this rapidly evolving field. It begins with a comprehensive introduction to deep learning, emphasizing its significance and the transformative impact it has had across various industries. Designed to be accessible to both beginners and experienced practitioners, the book systematically guides readers through the essential concepts and mathematical foundations that underpin deep learning, such as linear algebra, calculus, probability, and information theory. This book then delves into the fundamentals of deep learning, offering a detailed look at supervised, unsupervised, and reinforcement learning. It also covers key evaluation metrics, providing readers with the tools needed to assess the performance of different models effectively. The book explains complex concepts like activation functions, feedforward and backpropagation algorithms, and loss functions in a clear and accessible manner.Overall, this book serves as a comprehensive guide to understanding and applying deep learning.

Autorentext
Dr. Sharon Susan Jacob is working as Assistant Professor in the School of Data Analytics, Mahatma Gandhi University. Her prime interests are in the areas of bigdata analytics, deep learning, python programming, data mining and databases. She has many publications in reputed national and international journals and conferences.