DEEP LEARNING

Deep Learning provides a comprehensive introduction to the field, covering both theoretical concepts and practical techniques. It includes foundational topics like linear algebra, probability theory, and machine learning, and explains advanced methods such as deep feedforward networks, convolutional networks, and sequence modeling. The book also explores applications in areas like natural language processing, computer vision, and bioinformatics, while offering research perspectives on topics like autoencoders, deep generative models, and approximate inference. Ideal for students and software engineers, it also offers supplementary materials online for both readers and instructors.