For anyone interested, I have been really interested in deep learning and have been using the following resources:

For image processing (CNN) https://cs231n.github.io/

For natural language processing (RNN) http://cs224d.stanford.edu/syllabus.html

I also found the following coursera helpful https://www.coursera.org/course/neuralnets https://www.coursera.org/learn/machine-learning

These are great resources. There is also a ML class offered by Georgia Tech at Udacity under the OMSCS program. https://www.udacity.com/wiki/ml/GT

Thank you, a lot of the papers in DL/Sparse Coding aren't as clear as the sources you shared.

Does anyone know how to access past courses on coursera if you didn't sign up in a timely manner?

I think you just sign in and click on a big green button "Go to Course" on the right - at least it worked for me with neuralnets course by Hinton, which is really great.

It depends on the course, some courses are kept available and others are shut down after the action is over. I think the choice is left up to the instructors. I started downloading all of the videos when I sign up for a course for this reason actually, because I tend to go through them too slowly to finish with the main group.

Thanks. I really appreciate it when folks like you on HN post learning materials in the comments.

Thanks, i will see it, seems very interesting.

Is there a similar tutorial or resource that shows how to do face detection (not recognition) using deep learning. I came across tutorial from kaggle [1] but I'm looking for something that uses ML to detect where the faces are in an image.

[1] - http://danielnouri.org/notes/2014/12/17/using-convolutional-...

> If you are not familiar with these ideas, we suggest you go to this Machine Learning course and complete sections II, III, IV (up to Logistic Regression) first.

What is the link to that course?

The group is held by Prof. Ng which has a ML course on coursera https://www.coursera.org/learn/machine-learning - lecture notes of the in class course are here http://cs229.stanford.edu/materials.html (but without the lecture it may be.. without the lecture:))

Ng is one of those great profs that combine the rare gifts of being able to teach as well as be a leader in the field at the same time. I've had profs that are far less accomplished and far more arrogant. There's probably a correlation between those features, now that I think about it.

Definitely worth it. I had to drop out around week 5 due to a lack of time, but just started a new job that'll take me back to 40-hour weeks (down from 80-100), so I'll give it another shot next time they run the course.

The latest session started today, from 5 Oct to 27 Dec.

I assume it is http://cs229.stanford.edu/

That is normal class, not online if I'm correct.