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Literature Survey in Deep learning

The explosive growth of data and remarkable development in hardware technologies have intended the emergence of deep learning. Deep learning refers to a sub-field of machine learning techniques that seek to learn several levels of representation and abstraction that makes sense of data like text, sound, and image. Deep learning has been consistently recognized as a potential solution to the stumbling block of machine learning. The deep learning techniques accomplish the feature extraction in an automated manner, which enables scientific experts to capture the discriminating features with minimal human effort and domain knowledge. Thereby, the deep learning algorithm has been perceived as a highly promising decision-making algorithm. Notably, deep learning performs well in the autonomous driving task. This progress is primarily due to the various architectures of deep learning includes Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Belief Networks (DBN), Deep Stacking Networks (DSNs) and so on. The deep learning solution brings outstanding results in disparate real-time applications such as Natural Language Processing (NLP), speech recognition, image recognition, and computer vision. With the effective participation of image and audio processing features, deep learning methods emerged and are contributing in many fields including automotive, medical applications, military, education, and surveillance fields.