Deep training (color)

Fundamentals of applied mathematics and machine learning
The theory of probability and information theory
Assessment of maximum credibility
Modern approaches to deep networks
Regularization in deep learning
Optimization in the training of deep models
Squad modeling
Research on deep learning
Structural probabilistic models in deep training
Overcoming difficulties associated with a static amount
Deep learning is a type of machine learning that endows computers with the ability to learn from experience and understand the world in terms of hierarchy of concepts. The book contains mathematical and conceptual foundations of linear algebra, the theory of probability and the theory of information, numerical calculations and machine learning to the amount that is necessary for understanding the material. Descriptions of deep learning are described in practice, including deep direct distribution networks, regulator, optimization algorithms, adherent networks, modeling sequences, etc. Such applications as processing natural languages, speech recognition, computer vision, online letters of letters, bioinformatics are considered And video games.
The publication is intended for university students and graduate students, as well as experienced programmers who would like to apply deep training as part of their products or platforms.
The book is published in color and in solid binding