ABSTRACT
In the recent decades, the application of “machine learning”, “deep learning” and “artificial intelligence” has become widespread. In pattern recognition, deep artificial neural networks and machine learning have a wide range of applications. All the phrases are used often, sometimes interchangeably, with differing significance, in science and in media. Artificial intelligence is the technology that comprises science and engineering in making intelligent computer programs that make intelligent machines. Here, strong artificial intelligence makes use of machine learning and deep learning. Without programming explicitly, the ability of a system that improves by learning automatically and from its own experience is named as machine learning. Deep learning is an increasing field of research in machine learning (ML). And it is a subfield of machine learning that uses algorithms inspired by human brain with the help of large data sets using artificial neural networks. It consists of several occult layers of artificial neural networks. The approach of profound study employs high-level models and nonlinear transformations in huge databases. Recent improvements in artificial intelligence using deep learning architectures in several sectors have already contributed significantly to this. This study seeks to elucidate the link between the above words and to specify, in particular, the contribution to artificial intelligence by machine education and profound learning. We examine the relevant literature and provide a conceptual framework that explains the role of machine learning and profound learning in the development of intelligent (artificial) beings. In addition, the higher and more advantageous approach and the hierarchy of deep learning are given in layers and nonlinear operations in common applications and contrasted with the more standard techniques. We therefore want to give additional terminology and a basis for (interdisciplinary) talks.
