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-deep-learning
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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