
MIT 6.S191: Introduction to Deep Learning - say_it_as_it_is
http://introtodeeplearning.com/
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melling
There are a few years of this course. I list them here:

[https://github.com/melling/MathAndScienceNotes/blob/master/m...](https://github.com/melling/MathAndScienceNotes/blob/master/machine_learning.md)

Direct link to 2018-2019:
[http://www.youtube.com/watch?v=5v1JnYv_yWs&list=PLtBw6njQRU-...](http://www.youtube.com/watch?v=5v1JnYv_yWs&list=PLtBw6njQRU-
rwp5__7C0oIVt26ZgjG9NI)

2017:
[https://www.youtube.com/playlist?list=PLkkuNyzb8LmxFutYuPA7B...](https://www.youtube.com/playlist?list=PLkkuNyzb8LmxFutYuPA7B4oiMn6cjD6Rs)

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caycep
This may be slightly OT, but my layperson perspective is for a pre-eminent
STEM university, that MIT always seems to lag behind its rivals in deep
learning and the latest ml advances were spearheaded at Stanford/Silicon
Valley outfits, the Montreal/Toronto academic communities, Caltech and to some
extent in the UK. Whereas its background should have made it one of the
leaders in the field. Am I overlooking something, or just gross misinformed?
Did MIT culturally stay married for a while to the Minsky school of
deterministic AI vs. the Big Data statistical approach?

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mrdrozdov
Publications at NeurIPS/ICML is a reasonable proxy for contribution to the
field. [https://medium.com/@chuvpilo/ai-research-
rankings-2019-insig...](https://medium.com/@chuvpilo/ai-research-
rankings-2019-insights-from-neurips-and-icml-leading-ai-conferences-
ee6953152c1a)

In 2019, the top 5 institutions were:

    
    
      1. Google (USA) — 167.3
      2. Stanford University (USA) — 82.3
      3. MIT (USA) — 69.8
      4. Carnegie Mellon University (USA) — 67.7
      5. UC Berkeley (USA) — 54.0

~~~
etrk
Citations tend to be a better indicator of impact. There are some interesting
analysis here [1]. MIT has the most citations, and they've also published the
most papers by far. If considering citations per paper, MIT falls in the
middle of the pack. Toronto is an outlier with a very high number of citations
per paper.

NeurIPs is a big conference covering lots of topics, though. All of this says
relatively little about MIT's true impact on deep learning in the last eight
years.

[1] [https://www.microsoft.com/en-
us/research/project/academic/ar...](https://www.microsoft.com/en-
us/research/project/academic/articles/neurips-conference-analytics/)

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_coveredInBees
Looks neat. Personally bummed that it goes with Tensorflow, though I guess
that may be related to the course being sponsored in part by Google. Pretty
much all the latest research is being published in Pytorch and even OpenAI
switched to Pytorch recently.

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ganeshkrishnan
any courses like this that teach pytorch? I am thinking of taking up this
course and would like to give pytorch a shot too

~~~
_coveredInBees
CS231n from Stanford uses Pytorch

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bor100003
Sorry for the layman's questions but what's the difference between this course
and Lex Fridman's ?

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drongoking
Any idea on how this compares with the other deep learning intro courses
available?

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ksharifbd
Great! Will check that out.

