Stanford CS231n – Convolutional Neural Networks for Visual Recognition

http://cs231n.github.io/

by dennybritz • 11 years ago

125 11 11 years ago

cs231n.github.io

OT: It's fantastic that faculty at top universities like Stanford are increasingly making their content freely available on Github. It surely is an awesome time to be alive!

karpathy3• 11 years ago

Hi HN, the course is still in progress so the notes are not yet finished (I'm currently struggling to finish the ConvNet notes).

Our syllabus is here: http://cs231n.stanford.edu/syllabus.html where you can also find lecture slides, which have some more information.

Lastly, our assignments (that walk you through implementing a Softmax/SVM classifier and Neural Networks and ConvNets in Python+numpy) are all on terminal.com. Terminal.com lets us set up a VM in the browser: You visit the assignment URL, fork the snapshot, and you can work right away on the assignment in your browser on an IPython Notebook: the data is there, all dependencies are already installed, and everything ready to go. We're also working with terminal.com right now to get access to GPU machines soon, which will let us set up assignments that use Caffe and efficient GPU code, etc.

ac166• 11 years ago

This is brilliant, thank you! Is there any audio/video of the lectures available anywhere? I've been enjoying some other Stanford courses on iTunes U and Coursera :)

karpathy3• 11 years ago

Thanks! We ended up not recording the lectures this time around. I was playing with the idea of prerecording some videos MOOC style, but then ended up completely swamped even without them. Among the notes, slides, lectures, office hours, midterm design, coding assignments, project design, message boards, meetings, and various misc, running this class has turned out to be a stressful 100+ hours/week endeavor, and that's even with an all-star TA team by my side.

There have been some whispers of offering this class next year as a proper MOOC, in which case we'd definitely have videos. I'm just not sure if I'm up for it yet - I enjoy dissemination but I'm also starting to miss research quite a bit, and a MOOC would likely be the same thing or worse all over again.

mjrpes• 11 years ago

I've been reading through the notes and you present the material extremely well. I especially like how you discuss naive approaches before going about a better way to do things (e.g., computing a gradient numerically vs analytic). This is rare in teaching but, from a student's perspective, it really helps fill in the gaps of knowledge as you try to reason and understand the process on your own.

karpathy3• 11 years ago

Thanks! Unfortunately a lot of teaching is very relative and strongly depends on prior background. A different student gave me feedback on that section as: "Why are you expanding out all the random steps nonsense? Gradient descent takes one line to explain". It's the same for my lectures: No matter what I say or cover, at any point during the lecture some people are bored and some are completely lost. All you can hope for is hitting the median well and then learning to ignore (to some degree) the person who just asked a question that indicates that they are not following at all, and the person next to them who is yawning and on their phone.

tim_sw• 11 years ago

I've been following along, and this course has been great so far. The author also has made the code for one of his publications about image captioning publicly available: https://github.com/karpathy/neuraltalk

Along with various other nice work about neural networks:

http://cs.stanford.edu/people/karpathy/convnetjs/started.htm...

http://cs.stanford.edu/people/karpathy/recurrentjs/

http://karpathy.github.io/neuralnets/

Nice visualization about classifiers from the class (various svm, softmax):

http://vision.stanford.edu/teaching/cs231n/linear-classify-d...

hcarvalhoalves• 11 years ago

I wish I could take this class online (not an alumnus, not even in the US) but thanks for the free material!

Stanford CS231n – Convolutional Neural Networks for Visual Recognition

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