Deep Learning – State of the Art (2019)

Deep learning, which is an immensely rich and hugely successful sub-field of machine learning, is evolving at such a rapid pace that unless you are in the academia, it is often hard to keep track of the latest developments. I was, therefore, thrilled when I came across a video recording of a lecture (titled “Deep Learning State of the Art (2019) – MIT“) that was recently given by Lex Friedman, a research scientist at Massachusetts Institute of Technology (MIT). In this 45-minute long lecture, Lex goes through a number of recent developments in deep learning that are defining the state of the art in the field of algorithms, applications, and tools. I have provided the link to this video recording in this article, which I hope you will find useful.

Outline of the lecture:
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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