Introduction to Deep Learning
Deep learning (in other words, deep learning, deep learning, or hierarchical learning) is a subcategory of machine learning based on a set of algorithms that are trying to high-level abstract concepts in the data dictionary Which models this process using a deep graph that has several layers of processing consisting of several layers of linear and nonlinear transformations. In other words, it's based on learning to display knowledge and features in the model layers.
An educational sample (for example, a cat's image) can be modeled in a variety of forms, such as a mathematical vector filled with the value per pixel, and more generally in the form of a set of smaller shapes (such as cat facial members). Some of these modeling techniques simplify the learning process of the machine (for example: detecting a cat image). In deep learning, there is hope to replace the extraction of these human-like features (such as cast members) with full automated observation and semi-monitoring techniques. The initial impetus to this learning structure has been inspired by the examination of the nervous structure in the human brain in which the neural cells allow perception by sending messages to each other.
This book examines the fundamentals of deep learning, which attempts to accurately and easily address issues in this field.