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Yann LeCun, Yoshua Bengio, Geoffrey E. Hinton | Nature | (2015)

Key Takeaways

Sample Definition And Size

This is a review article discussing deep learning methods broadly; it does not involve a specific empirical sample or dataset, and thus no sample size is applicable.

Study Type

Review article (Review of methods and developments in deep learning) ([nature.com](https://www.nature.com/articles/nature14539?utm_source=openai)).

Conflicts Of Interest

No conflicts of interest are declared in the article; the Nature page does not include any COI statement ([doi.org](https://doi.org/10.1038/nature14539)).

Results Summary

The article summarizes the principles and breakthroughs of deep learning, including multilayer neural networks, backpropagation, convolutional and recurrent networks, and their impact on domains such as speech recognition, visual object recognition, object detection, drug discovery, and genomics ([nature.com](https://www.nature.com/articles/nature14539?utm_source=openai)).

Abstract

No abstract available

Referenced In

Just listened to this podcast on StarTalk, with AI pioneer Geoffrey Hinton. One thing covered was Geoffrey Hinton's 2015 Paper in Nature "Deep Learning", which (if I understand correctly!) discovered or explained how ai can "learn" by itself through "backpropagation". (Also, check out the lead author of the paper!)

Is AI Hiding Its Full Power? With Geoffrey HintonLots of other cool themes discussed and questions answered – give it a listen!

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