About this Computer Science article
Deep learning in neural networks: An overview by Jürgen Schmidhuber is a Computer Science article available to read on EtoBox.
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes 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.
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Jürgen Schmidhuber
- Published
- 2015
- Language
- EN
- Field
- Computer Science (Physical Sciences)