In-depth learning

About this book

In-depth learningb A complete reference book and bible for in-depth study! b A book that introduces various topics of in-depth study. This course introduces several key concepts of linear algebra, probabilistic theory, information theory, numerical computation, and machine learning related to in-depth learning, and then introduces several concepts used by industry practitioners such as in-depth forward neural networks, regularization, optimization algorithms, It explains in-depth learning techniques and introduces realistic in-depth learning practice methodology. It also outlines methods for applying in-depth learning for natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and video games. Finally, we examine in-depth learning from the point of view of the research, such as the theory of linear factors, automatic encoders, expressive learning, structural probability models, and Monte Carlo methods.