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Comment by striker_axel | Hacker News Reader

I would recommend Andrew Ng's Deep Learning Course. https://www.coursera.org/specializations/deep-learning

This course is extremely good mostly because it covers the essential theoretical topics and gives some practical advice. TIP: do solve the assignments bcz it will clear a lot of concepts while solving it. ( or other solution can be found on github )

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I strongly second that and also Andrew Ng's : Machine Learning course.

The only thing that was annoying for me was that the Jupyter assignment auto-grader would incorrectly fail correct answers and it's not always easy to debug the reason why it failed. If the python syntax deviates too much from the expected answer, it also can cause some issues. Please note: I am a very experienced programmer and have been using python for more than a decade. This was not my first rodeo...

Otherwise this should in no way be a deal breaker, the material and assignments are top-notch. The forums are also helpful in finding out issues with the auto-grader.

Worth every penny and minute invested!

Can you comment on whether it'd be best to jump straight into the deep learning course, or is it better to do the machine learning course first?

The deep learning course is taught in a way where you don't need the machine learning course first, so it's possible to start with either, especially if you have any familiarity with ML. Deep learning is one specific type of machine learning so there is alot of other techniques you will be missing if you only do the deep learning course though.

I went through it, really enjoyed it. But I had experience in the same subject matter before taking it. If you have some ML experience, I would recommend diving straight in for a good breadth-first look at deep learning topics. If you don't have any ML experience or don't really know the concepts, I would recommend taking their other course first (Intro to AI, or AI for Everyone, or w/e -- which I skimmed to see if it was something I should recommend to others, and I liked it).

I think it would be useful to take Udacity’s machine learning class as well to provide an additional perspective. There are some parallels such as edge detection uses the same techniques as convolutional networks, regularization is a general technique useful for both neural networks and clustering, optimization of steps in planning probably has a parallel to how Alpha Go works. Particle filters was cool.

Also, take several of the classes in the specialization. Don’t stop at the first course. Convolutional networks has been great.

I liked working on the notebooks and watching the interviews with some of the pioneers of Deep Learning.

Can anyone who's taken that and MIT's deep learning course (introtodeeplearning.com) compare them?

How much do the material overlap? Which one is better?

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