Deep learning is a branch of machine learning (machine learning-ML). Deep learning methods utilize high-level model abstraction of nonlinear transformations in large databases. In other areas, the implementation of deep learning architectures has contributed significantly to the development of artificial intelligence. This paper presents recent research on newly applied deep learning algorithms. Convolutional Neural Networks are used in deep learning. Database Management System PostgreSQL object-relational database.
The implementation resulted in achieving the set goals and objectives. The method of analyzing the input data is described, the differences between machine learning and deep learning are explained, and an example of classifying an image representing a sign language image using logistic regression, one of the deep learning algorithms, is presented. Deep neural networks can work with the full set of available data better than alternative approaches. During the learning process, the neural network itself determines which features in the data are important and which are not. Artificial neural networks can predict symptoms that humans cannot. Thus, with the help of deep neural networks, we can solve problems that traditional machine learning algorithms cannot perform.
Key words: deep learning, methodology, artificial intelligence, machine learning, neural network, object recognition.
