
Stanford CS231n – Convolutional Neural Networks for Visual Recognition - dennybritz
http://cs231n.github.io/
======
karpathy
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](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.

~~~
mjrpes
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.

~~~
karpathy
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.

~~~
agibsonccc
I'd like to second karpathy here. When you do a practical interpretation of
something like machine learning (and even deep learning!) I've had to cater to
different tastes. Usually people in these classes fall in to either the more
engineering side where breaking down gradient descent can help, or mathy where
they've already done convex optimization and know the trade offs of LBFGS vs
Conjugate Gradient and all properties of parametric models are obvious. The
best thing you can do here is work with the students 1 on 1 to fill in the
gaps. There's no silver bullet. Which is why I'd say taking the class in
person is always going to be better than notes. I think karpathy is hitting a
wider audience with the way he's handling the notes though.

Props to the way you're handling this!

~~~
agibsonccc
Confused as to the downvote..but maybe I can clarify here. People wanting to
apply deep learning tend to fall in to one of two camps: heavy CS with some
applied ML experience and mathematicians who might not have as much experience
building things. In karpathy's case, he's likely going to get a mix of
students who have taken different classes. There's a lot of variance either
way.

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tim_sw
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](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/convnetjs/started.html)

[http://cs.stanford.edu/people/karpathy/recurrentjs/](http://cs.stanford.edu/people/karpathy/recurrentjs/)

[http://karpathy.github.io/neuralnets/](http://karpathy.github.io/neuralnets/)

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

[http://vision.stanford.edu/teaching/cs231n/linear-
classify-d...](http://vision.stanford.edu/teaching/cs231n/linear-classify-
demo/)

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krat0sprakhar
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!

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

