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An overview of deep learning architectures, libraries and its applications areas

In today's world, a massive amount of data is available through various fields in the form of text, images and audio. All this data can be a vast repository of information when analysed to find some patterns, trends and predictions. Extracting features from a corpus using traditional statistical methods was a challenging task and then prediction algorithm or clustering was applied on data to find useful information. Now the scenario has changed with the involvement of deep learning. Using deep learning model can be trained, learned on complex data along with multiple levels of abstraction. This paper gives an overview of some mostly used deep learning architectures, libraries which are useful and discusses various application areas where deep learning is active.