Personally, I've found that I don't retain much of this sort of material without working through exercises. If you learn the same way, you might want to check out the series of progressive exercises from Andrew Ng here: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
For reference, I have a copy of my solutions here: https://github.com/danluu/UFLDL-tutorial. Debugging broken learning algorithms can be tedious in a way that's not particularly educational, so I tried to find a reference I could compare against when I was doing the exercises, and every copy I found had bugs. Hope having this reference helps someone.
Comments
Personally, I've found that I don't retain much of this sort of material without working through exercises. If you learn the same way, you might want to check out the series of progressive exercises from Andrew Ng here: http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
For reference, I have a copy of my solutions here: https://github.com/danluu/UFLDL-tutorial. Debugging broken learning algorithms can be tedious in a way that's not particularly educational, so I tried to find a reference I could compare against when I was doing the exercises, and every copy I found had bugs. Hope having this reference helps someone.
For some more elementary material, I also recommend Andrew Ng's machine learning course on Coursera. He's a great teacher.
Link for the impatient https://www.coursera.org/course/ml Looks great indeed!
Nice!
awesome - thanks for the links