Deep Learning: Principles and Implementations

A hands-on and intuitive guide to the foundations of modern deep learning In Deep Learning: Principles and Implementations, distinguished researcher and professor Weidong “Will” Kuang delivers an up-to-date exploration of how major deep learning algorithms and architectures are formalized and developed from mathematical equations. The book bridges theory and practice and covers a wide range of fundamental topics, including linear regression, logistic regression, basic neural networks, convolution neural networks, as well as other basic and advanced subjects in the field. The author provides intuitive introductions to each subject and presents the development of algorithms and architectures from basic mathematical concepts. Along the way, he relies on straightforward math to keep the topics accessible for non-mathematicians and accompanies his explanations with tested Python sample code you can apply in your own work. You’ll also find: Thorough introductions to both linear and logistic regression, offering a solid foundation and insight into neural networks Comprehensive explorations of neural networks, computer vision, natural language processing, generative models, and reinforcement learning Practical exercises that students and practitioners can use to apply and develop the concepts found in the book Balanced treatments of the mathematics, algorithms, architecture, and code that serve as the foundations of a complete understanding of deep learning Perfect for undergraduate and graduate students with an interest in deep learning, Deep Learning: Principles and Implementations will also benefit practicing software engineers, faculty, and researchers whose work involves deep learning and related topics.