Deep Learning in Neural Networks: An Overview

Deep Learning in Neural Networks: An Overview by Jüergen Schmidhuber.


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.

A godsend for any graduate student working in deep learning! Not only does Jüergen cover recent literature but he also traces the ideas back into history. Fortunately for all of us interested in the history of ideas in computer science, both the LATEX source, DeepLearning8Oct2014.tex and the BIBTEX file deep.bib are available.

Be forewarned that deep.bib has 2944 entries.

This is what was termed “European” scholarship, scholarship that traces ideas across disciplines and time. As opposed to more common American scholarship in the sciences (both social and otherwise), which has a discipline focus and shorter time point of view. There are exceptions both ways but I point out this difference to urge you to take a broader and longer range view of ideas.

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