MIT 6.S094: Introduction to Deep Learning and Self-Driving Cars

22 thoughts on “MIT 6.S094: Introduction to Deep Learning and Self-Driving Cars”

1. Damn, that General Purpose Intelligence shit is good. Magnificent video. Liked and subscribed! The way you explain and describe it, it's so simple but so deep. Love it.

2. There have been so many "winters" for AI, that you could almost train a neural network to predict them xD

3. 42:49

4. Tokyo and Europe are lacking rules, good roads and street markings? Less requirements? Have you heard of the German Autobahn? Have you driven a car in Tokyo? Dude… You should really get out more…

5. Didn't know that guy from Homeland is an AI professor!!

6. I thought joe was going to pop out and start talking over him

7. guys please help me on this – i had decided to choose the deep traffic and deep tesla as a part of my mini project
   well i wanna know whether after watching all the lectures in the playlist will i be in a position to solve the deep traffic project
   i want to know whether these lectures are enough to solve the deep traffic problem since i have just 8 days for my project deadline
   please reply

8. Reasoning maybe is as simple as learning from data ……..tantalising

9. Hi Lex,

   Thank you so much for the wonderful courses. Really great !!

   Also, for students like us who like to understand deeper on how to build the Deep Learning for self driving cars algorithms, do you also provide the code(Python?) which we can take a look at?

   Thanks again !

10. 와 이사람 진짜 열심히 가르친다… 한국에도 이런 교수 있으면 좋을듯.. 한국에서 같이 공부하실 분 연락주세요 ^^neverfok@ 네이버입니다. ^^

11. The general purpose intelligent pong game playing network does beat the computer but it goes up and down like a drunk guy. That's expensive. How can we modify the training algorithm to also conserve energy?

12. Take whatever the most powerful GPU can do graphically. Now, imagine that backwards. Matrix transformations on each layer depend on every other layer simultaneously. Apparently continuous values were only suggested this year as opposed to discrete.

13. wow how old is he? he teaching at mit wow

14. 1:02:06 I don't know what language that is, probably one of the Nordics. Definitely not German, which would be something like dunkel Schokolade.

15. Sir, I am a researcher from Pakistan working on self driving cars using deep learning. Your way of teaching is excellent and very effective.

    I need the presentations slides if you may share with me.

16. beside he outstanding at computer vision and autonomous car,i just wonder how he speak in russian.

17. Thanks so much for videos.

18. thx for sharing.

19. I know this is an old lecture but I will still comment on it. Watching the pingpong game of Andrej Karpathy I must say that the ML racket is moving most of the time in the lower and middle left area of the play field. Once the ball is sent in the upper left corner the human scores. Another thing is that the ML racket moves faster, it has а higher frequency of movement, meaning that it is not predicting balls position but rather focusing on speed of movement. Then again all movements in this simple game are geometrical and easy ti predict. I would say that the same result can be achieved without a nerion network and ML. I do not think this game and the result of it is impressive at all or it can serve as a good example of how good are neuron networks.

20. Love these lectures.
    bookmarked 44:00 1:03:00

21. 8:47 – I didn't realize Artificial Intelligence had been born yet… Unless, of course, we are talking by concept – conceptually it is very easy and has been very easy…I think Turing would agree.

    But, if the concept is guided by a question asked and answered in a 1996 Time article… yeah, if I think about it, I guess it was pretty difficult. But that answer to the question is a false assumption from the outset. That's why A.I. today isn't A.I. There are intelligent systems that have developed, and I imagine it was difficult getting there. …It was indeed difficult getting to not A.I. – that seems accurate.

    And listening to the interpretation of AI sufficiency of Turing explained by this dude, it's clear A.I. isn't even on the damn radar. …Even where Neural Networks are today, how he so non-elloquently transitions to from Turing, are developed completely missing the point. The state of the art is a failure as far as A.I. is concerned.

    Listen to this dude, you'll never get to Artificial Intelligence. It's upsetting this is where the next generation is being taken to with respect to AI. As someone who has studied AI longer than this dude inspired in HS in 1996, it's amusing…but at the same time a goddamn tragedy…

    My graduate advisor would puke in his soup listening to this… But, that's the World's foremost Epiphenominologist (former; retired), a scholar of A.I. longer than this dude has been alive, and a former graduate assistant to Karl Popper.

    …I suppose a lecturer is just a lecturer, even if it is MIT…

22. Nice slides but there is little information about self-driving cars, just some superficial concepts about AI, it is more like a Advertisement of AI rather than a lecture.

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