Understanding DL basic concepts

DL is a branch of ML based on using algorithms to model high-level abstractions about data. This discipline is part of a range of approaches that aim to learn methods for representing data. For example, an observation such as an image can be described in different ways: as a vector of intensity values for each pixel, or more abstractly as a set of edges or regions that have shapes or significant features. Some of these possible representations may prove more effective than others in facilitating the process of training another ML system.

For automatically identifying and extracting relevant features from raw data, we can use automated feature extraction to eliminate the need for manual feature engineering (FE). This process streamlines ML tasks and improves model performance.

Automated feature extraction

In this context, one of the central aspects of DL is the development of learning algorithms that specialize in automatically extracting significant...