A review on advances in deep learning

Over the years conventional neural networks has shown state-of-art performance on many problems. However, their performance on recognition system is still not widely accepted in the machine learning community because these networks are unable to handle selectivity-invariance dilemma and also suffer from the problem of vanishing gradients. Some of these issues have been addressed by deep learning. Deep learning approaches attempt to disentangle intricate aspects of input by creating multiple levels of representation. These approaches have shown astonishing results in problem domains like recognition system, natural language processing, medical sciences, and in many other fields. The paper presents an overview of different deep learning approaches in a nutshell and also highlights some limitations which are restricting performance of deep neural networks in order to handle more realistic problems.