Deep Learning New Frontiers

Deep Learning New Frontiers

MIT Introduction to Deep Learning 6.S191: Lecture 6 with Ava Soleimany.

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Lecture Outline

  • 0:00 – Introduction
  • 0:58 – Course logistics
  • 3:59 – Upcoming guest lectures
  • 5:35 – Deep learning and expressivity of NNs
  • 10:02 – Generalization of deep models
  • 14:14 – Adversarial attacks
  • 17:00 – Limitations summary
  • 18:18 – Structure in deep learning
  • 22:53 – Uncertainty & bayesian deep learning
  • 28:09 – Deep evidential regression
  • 33:08 – AutoML
  • 36:43 – Conclusion

Efficient Computing for Deep Learning, Robotics, and AI

Efficient Computing for Deep Learning, Robotics, and AI

Lex Fridman shared this lecture by Vivienne Sze in January 2020 as part of the MIT Deep Learning Lecture Series.

Website: https://deeplearning.mit.edu
Slides: http://bit.ly/2Rm7Gi1
Playlist: http://bit.ly/deep-learning-playlist

LECTURE LINKS:
Twitter: https://twitter.com/eems_mit
YouTube: https://www.youtube.com/channel/UC8cviSAQrtD8IpzXdE6dyug
MIT professional course: http://bit.ly/36ncGam
NeurIPS 2019 tutorial: http://bit.ly/2RhVleO
Tutorial and survey paper: https://arxiv.org/abs/1703.09039
Book coming out in Spring 2020!

OUTLINE:
0:00 – Introduction
0:43 – Talk overview
1:18 – Compute for deep learning
5:48 – Power consumption for deep learning, robotics, and AI
9:23 – Deep learning in the context of resource use
12:29 – Deep learning basics
20:28 – Hardware acceleration for deep learning
57:54 – Looking beyond the DNN accelerator for acceleration
1:03:45 – Beyond deep neural networks