Published on January 18, 2019 by

New lecture on recent developments in deep learning that are defining the state of the art in our field (algorithms, applications, and tools). This is not a complete list, but hopefully includes a good sampling of new exciting ideas. For more lecture videos visit our website or follow code tutorials on our GitHub repo.

INFO:
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-dee…
Slides: http://bit.ly/2HiZyvP
Playlist: http://bit.ly/deep-learning-playlist

OUTLINE:
0:00 – Introduction
2:00 – BERT and Natural Language Processing
14:00 – Tesla Autopilot Hardware v2+: Neural Networks at Scale
16:25 – AdaNet: AutoML with Ensembles
18:32 – AutoAugment: Deep RL Data Augmentation
22:53 – Training Deep Networks with Synthetic Data
24:37 – Segmentation Annotation with Polygon-RNN++
26:39 – DAWNBench: Training Fast and Cheap
29:06 – BigGAN: State of the Art in Image Synthesis
30:14 – Video-to-Video Synthesis
32:12 – Semantic Segmentation
36:03 – AlphaZero & OpenAI Five
43:34 – Deep Learning Frameworks
44:40 – 2019 and beyond

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