Semantic Scholar Open Access 2014 17359 sitasi

Deep learning in neural networks: An overview

J. Schmidhuber

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science Medicine

Penulis (1)

J. Schmidhuber

Format Sitasi

APA MLA BibTeX

Schmidhuber, J. (2014). Deep learning in neural networks: An overview. https://doi.org/10.1016/j.neunet.2014.09.003

Akses Cepat

Lihat di Sumber doi.org/10.1016/j.neunet.2014.09.003

Informasi Jurnal

Tahun Terbit: 2014
Bahasa: en
Total Sitasi: 17359×
Sumber Database: Semantic Scholar
DOI: 10.1016/j.neunet.2014.09.003
Akses: Open Access ✓