
CS231n: Convolutional Neural Networks for Visual Recognition - sdenton4
http://cs231n.stanford.edu/syllabus.html
======
dicroce
One of the teachers of that class (Andrej Karpathy) has a blog with the best
introduction to neural nets I've seen yet:

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

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kornish
Those interested in CS231n may also find CS224d, Deep Learning for Natural
Language Processing, to be interesting. There are notes and lecture videos
online for that class as well (or at least there will be as the class
proceeds):

[http://cs224d.stanford.edu/syllabus.html](http://cs224d.stanford.edu/syllabus.html)

~~~
neel8986
+1 for CS224d. Richard Socher is a great teacher and you can understand almost
the entire class with very little background in NLP and Deep learning. However
basic matuarity in LA will be helpful.

Both CS224d(NLP) and CS231n(vision) provides a great practical introduction to
Deep learning. Thanks Stanford for proving such high quality material for free

~~~
knicholes
With an IQ of just 125 and a BS in CS, I've struggled with this CS224d class.
I've gotten half-way twice realizing that I don't know enough to learn more. I
told myself, "Just keep going through the course and you'll eventually get
it." This still isn't the case. I'll have to finish the videos and see what
happens next. I get the general idea, but I definitely couldn't implement an
artificial neural network after having just watched the videos. Perhaps
reading the suggested readings would make a big difference.

Seriously, though. When I look at assignment 1, I don't even know what I'm
supposed to do.

~~~
neel8986
Some background in Linear Algebra and calculus will be really helpful.

If you want to implement backprop from scratch just look at the segment around
[https://youtu.be/I2TfdXfSOfc?list=PL05WXsDr_SWRGcuy5LA5eNjCq...](https://youtu.be/I2TfdXfSOfc?list=PL05WXsDr_SWRGcuy5LA5eNjCq1JgcXYM1&t=4168)

He explained really well how you can automatically get the derivation without
going through painful calculation. Also i will suggest you to stick to the
class as second half contains less maths and more fun techniques kind of like
lego blocks :)

if you just want use neural net in production I highly recommend using
something like theano/ keras

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rilut
STAT212b: Topics Course on Deep Learning by UC Berkeley Statistics Department

[https://github.com/joanbruna/stat212b](https://github.com/joanbruna/stat212b)

