CS231n: Convolutional Neural Networks for Visual Recognition

Another resource worth checking for learning deep learning is Stanford's CS231 for deep learning. It is available here : http://cs231n.github.io/. All there examples are in python and numpy and offer a lot more practice with low level details of a Convolution Neural Network.

Stanford’s CNN course (cs231n) covers only CNN, RNN and basic neural network concepts, with emphasis on practical implementation. The only prerequisite for taking this course is a basic Machine Learning course along with some Mathematical background. The instructor Andrej Karpathy and his team have made the course self-contained and you will get enough background to start working on deep learning projects on your own.

In my opinion CS231n is probably the best introductory course into Deep Learning for beginners or specialists who want to refresh their memories on the theory. Even though the name the name implies focus on CV it provides good material of general methods and concepts of Deep Learning. You will benefit from the course even if your area of interests lies outside CV (e.g. text processing or speech recognition). Content specific to CV is introduced gradually in the first half of the course and intensifies in the second half. However, CV related content does not require deep prior knowledge to follow and still can provide valuable information regarding Convolutional Neural Networks or Recurrent Neural Networks. The course includes recent findings in the field and which is especially important for such a rapid changing area of research. Therefore, I recommend the course for everyone interested in entering the field of Deep Learning.

You could get a lot out of CS231n just by watching a few lectures on topics of interest. You will gain a deeper understanding of the content and have a chance to implement, train and fine-tune your own neural nets if you go through the assignments, but be prepared to spend a lot of time working your way through. This is a meaty course with over 20 hours of lecture and you may spend several times that amount on the assignments. The 3 assignments are very well made, but they are lengthy, with each having 4 to 5 separate notebooks you have to work through and many functions you have to create in separate files. The assignments provide a healthy amount of guidance and many tests to make sure your code is working so things don't break later on. Still, getting everything to work on your own can be challenging and the forums are only for Stanford students. Try searching online if you get stuck; there is a Reddit sub and various GitHub pages that can help you get through. It's worth completing the notebooks even if you have to copy some code, because you'll get to play around with some interesting applications like visualizing the filters on the top layer of your conv nets and warping images with your own version of DeepDream.

CS231n is everything Udacity's Deep Learning should have been. You'd be hard-pressed to find a better course on neural nets anywhere on the web or on a campus. It covers everything you need to understand neural nets, from high level concepts to low level implementation details and the focus on recent advances makes it more relevant than older courses on the same subject.

That is freaking awesome. Doing cs231n right now, and very happy about that.