Neural Networks for Machine Learning

The syllabus, draft though it is, indicates the second half of the class will focus on deep learning, a field of machine learning that has demonstrated huge potential.

There have been some huge developments in neural networks in the last few years, particularly with respect to deep learning. If you missed out on that you might want to try this class. Hinton has been involved in many of these advances.

The second half of the course appears to focus on deep learning topics so you might want to start there if you already know the basics.

"Many of you are unhappy with only being allowed to attempt a quiz once. Starting in week two, we have therefore decided to make up twice as many questions and to allow you to do each quiz twice if you want to. The second time you try it the questions will all be different. Your score will be the maximum of your two scores. For week one, the quizzes will remain as they are now.

Many of you would like the names of the videos to be more informative. We will change the names to indicate the content and the duration.

Some of you thought that some of the quiz questions were too vague. We will try to make future questions less vague.

Some of you are unhappy that we do not have the resources to support Python for the programming assignments. We sympathize with you and would do it if we could. You are still welcome to use Python (or any other language) if you can port the octave starter code to your preferred language. We have no objection to people sharing the ported versions of the starter code (but only the starter code!). However, if you get starter code in another language from someone else, you are responsible for making sure it does not contain bugs."

I thought that was pretty funny!

My one complaint is that the programming assignments weren't interesting at all. The results were interesting, but the setups were mostly given to us, and we just had to code an algorithm that was in our notes. For someone who understands the basics of linear algebra and programming, it was just a syntax challenge, and that got irritating after a bit so I stopped doing them.

I won't get the certificate for completing the course, but I have a few extra hours of free time each week to add this second course, so I'm happy. I doubt that the actual homework that Stanford students taking this course get is so easy and repetitive, though, and I'm positive they wouldn't complain about not getting to retake quizzes after getting poor grades.

Not to knock the course. I've learned a lot and the professor (Andrew Ng) does a good job.

If you are taking a class voluntarily over the Internet, what benefit would be gained by cheating? I presume that a large fraction of people who are doing volunteer coursework are doing it to learn, not to keep a GPA up for some other reason (sports eligibility, scholarship requirements, parental expectations, Etc.) so looking at other solutions on Github might actually enhance the experience for you if you look at other solutions. If you find a way to do it better than the other solutions that could be a goal in itself.

This is one of those things I find most intriguing about 'free' classes on the Internet, the value equation is shifted around.