Abstract
Deep learning prospered as a distinct era of research and fragment of a wider family of machine learning, based on a set of algorithms that strengthen to model high-level abstractions in data. It tries to imitate the human intellect and learns from complicated input data and resolve different types of difficult and complex tasks. Because of Deep Learning, it was successful to deal with different input data types such as text, sound, and images in various fields. Improvement in deep-learning research has already influenced the search for speech recognition, automatic navigation systems, parallel computations, image processing, ImageNet, natural language processing, representation learning, Google translate, etc. Here, we present a review of DL and its applications including the recent development in natural language processing (NLP).