MIT 6.S191 (2021): Recurrent Neural Networks

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Soleimany
January 2021

Lecture Outline
0:00​ - Introduction
2:37​ - Sequence modeling
4:54​ - Neurons with recurrence
12:07​ - Recurrent neural networks
14:13​ - RNN intuition
17:01​ - Unfolding RNNs
18:39 - RNNs from scratch
22:12 - Design criteria for sequential modelling
23:37 - Word prediction example
31:31​ - Backpropagation through time
33:40​ - Gradient issues
38:46​ - Long short term memory (LSTM)
47:47​ - RNN applications
52:15​ - Attention
59:24​ - Summary

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[nilotpalmrinal]

“The definition of genius is taking the complex and making it simple.” - A congruence of this quote and this lecture series defines the quality of Instructors. Thanks a ton to Alex and Ava!!! Thank you very much!

[VikasKM]

One of the clearest explanations on RNNs, LSTMs.

[cedricmanouan]

Even if I’ve already watched previous lectures, I am watching these ones as it is the first time. Masterpiece 😭❤️🐐

[lakshmisrinivas]

I really liked the way LSTM concept is explained. The attention mechanism has been briefly described yet well explained. Thank you so much.

[otakudnp]

Going from Attack on Titans to Deep Learning. What a week :)

[programmer]

Clear intro. to RNNs building up intuition from the basic principles Loved the lecture !

[aaronwilliams]

Incredible lecture, I had to pause halfway through just to absorb as much information as I could. Please keep these coming, I have a great aptitude for neural networks! This course is right up my alley :)

[sree_haran]

Thanks a lot for making these mit lectures public... I'm so happy to learn these.. it's all because of you 🤗

[reandov]

Thanks for the amazing class once again! Recurrent Neural Networks are very strong and important nowadays in our society, and the improvement and studies about them make a huge impact on this!

[normalhuman]

Will be honest, this is probably the best lecture I have ever seen on DL. Most other lectures are so inaccessible and jargon filled that they fail to drive home the fundamentals. Kudos to Ava and Alexander

[khalilrekik]

Going from WandaVision to Deep learning. What a weekend :D

[jonothan]

This is the best explanation of LSTMs I've seen!

[isaacguerreiro]

I would like to thanks Alex and Ava. Have this content with this quality is priceless for someone that is trying to learn ML and DL by himself. Thank you for share this incredible class online for free.

[DawidOhia]

Ava Soleimany has a really high level skills of knowledge explaining. Thanks for making these lectures public.

[zigzag]

Ava really cleared the confusing bits of the internal workings of standard RNNs and LSTMs. Thanks.

Thanks Ava and Alex.

[prashantkumar]

Our instructor's flow is super smooth, no cap

[harunismail]

MIT gives a title of introduction to deep learning, but some people realize that it is quite deep rationale behind what they have sought for a long time ago. Thank you MIT. A great lecture.

[ajaytaneja]

I just wait all day through at office to get back home and watch this amazing series of lectures. Thank you Team @Alexander Amini

[Jacob]

This lecture is perfection! I say that as a pedantic PhD 🙂. I can tell that a crap ton of work went into it.

[prashanthvaidya]

Grateful to you profs and MIT! 💯

What a wonderful introduction to the intuition behind RNNs. :)