22 thoughts on “Deep Learning for Computer Vision (Andrej Karpathy, OpenAI)”

  1. Ok I disagree with how he answered the woman's question. There is a way to choose a MINIMAL amount of layers, based on the complexity of the data. It depends on convexity vs non-convexity in the data space. non-convexity requires a minimum of 2 hidden layers to represent. This is why before you dig into deep learning and all the modern stuff a good basis in Universal Approximation Theory and NN's as Universal Approximators is critical!

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  3. Great video. I came here after completing 4 video sessions by Lex (MIT 6:S094). Thanks for compling these videos.

  4. Hi from Brazil!! Thank you for sharing this. It is a really good material for researchers and who is initiating in this area.

  5. slides: https://docs.google.com/presentation/d/1Q1CmVVnjVJM_9CDk3B8Y6MWCavZOtiKmOLQ0XB7s9Vg/edit#slide=id.p

  6. 27:02 How are the gradients computed?
    1) Gradients of the loss with respect to the activation
    2) Gradients of the mean/sum of activations with respect to the input image

  7. Extra comment, so the important link is visible without expanding:
    I´m looking forward to dig into the extended version, namely the CS231n Winter 2016 Lecture series referenced in the video. (at https://www.youtube.com/watch?v=g-PvXUjD6qg&list=PLlJy-eBtNFt6EuMxFYRiNRS07MCWN5UIA )

  8. Hey Lex,
    can´t thank you enough for splitting up the day-long streams! Much easier to consume — as I wanted to download to enjoy it on mobile!
    I came across Andrej Karpathy´s Deep Learning for Computer Vision yesterday.
    I´ve been trying to really understand CNNs and the Deep Learning paradigms technically for some time now and sucked up everything I could since July.
    Andrej´s lecture is the very best I found to get up to speed on most important details in the least possible time.


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