In deep learning, an artificial neural network (ANN) stores and processes large amounts of data. This is because artificial neural networks are used in deep learning. It is able to find both overt and covert connections across datasets. When working with deep learning, direct programming is not always necessary. Recent years have seen a meteoric rise in its popularity as a result of developments in processing power and the availability of massive datasets. This is one of the reasons why. For the reason that it was created using artificial designed to learn from large datasets. Deep Learning is a subfield of Machine Learning that use neural networks for modeling and problem solving; its development was spurred by the need to address complex problems. In order to train these networks to deal with challenging problems, the appropriate models must first be solved. Neural networks, which imitate the brain in structure and operation, process and transform data. These tasks are handled by multilayer neural networks consisting of numerous nodes communicating with one another. Fundamental to the idea which are defined by the existence of several layers of connected nodes. It is from this idea that the term "deep neural network" was coined. Because these networks can spot hierarchical patterns and features in the data, it's possible that they can develop elaborate representations of the data. If deep learning algorithms could independently learn and develop themselves depending on the data they were presented, then human engineers might not be needed to manually construct features. Deep learning has been very effective in several fields. These fields include picture identification, natural language processing, voice recognition, and recommendation systems. When training deep neural networks, it is generally necessary to have access to vast volumes of data and have a fast processing speed. Training deep neural networks, on the other hand, has become a great deal less complicated in recent years because to the proliferation of cloud computing and specialized equipment such as Graphics Processing Units (GPUs)