Deep Learning in Neural Networks: An Overview
It offers a comprehensive overview for researchers and practitioners in machine learning, but it is incremental as it primarily compiles existing knowledge without introducing new methods or results.
This paper provides a historical survey of deep learning in neural networks, summarizing relevant work from recent years and the previous millennium, including various learning paradigms like supervised, unsupervised, reinforcement, and evolutionary methods.
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarises relevant work, much of it from the previous millennium. Shallow and deep learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.