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Deep learning (DL) is a hot topic in machine learning (ML). To limit the amount of time and money spent on supervised machine learning, we use DL. With a variety of methodologies and topographies, DL may be applied to address complicated problems in a variety of contexts. Features that illustrate or differentiate are learned in a layered manner. When it comes to effective security solutions, DL has made significant strides in a wide number of application domains. The best alternative for revealing highdimensional data's complex architecture is to use the back propagation technique in this manner. DL is benefiting business, science, and government in a variety of applications such as Artificial Intelligence (AI) and ML, which can be applied to everything from cancer detection to stock market research to smart cities. As a result, the focus of this work is on the basic ideas and limitations of DL.