Quick Impression on deeplearning.ai (After Finishing Coursework)

Following experienced guys like Arvind Nagaraj​ and Gautam Karmakar​, I just finished all course works for deeplearning.ai. I haven't finished all videos yet. But it's a good idea to write another "impression" post.

* It took me about 10 days clock time to finish all course works. The actual work would only take me around 5-6 hours. I guess my experience speaks for many veteran members at AIDL.
* python numpy has its quirk. But if you know R or matlab/octave, you are good to go.
* Assignment of Course 1 is to guide you building an NN "from scratch". Course 2 is to guide you to implement several useful initialization/regularization/optimization algorithms. They are quite cute - you mostly just fill in the right code in python numpy.
* I quoted "from scratch" because you actually don't need to write your own matrix routine. So this "from scratch" is quite different from people who try to write a NN package "from scratch using C", in which you probably need to write a bit of code on matrix manipulation, and derive a set of formulate for your codebase. So Ng's Course gives you a taste of how these program feel like. In that regard, perhaps the next best thing is Michael Nielsen's NNDL book.
* Course 3 is quiz-only. So by far, is the easiest to finish. Just like Arvind and Gautam, I think it is the most intriguing course within the series (so far). Because it gives you a lot of many big picture advice on how to improve an ML system. Some of these advices are new to me.

Anyway, that's what I have, once I watch all the videos, I will also come up with a full review. Before that, go check out our study group "Coursera deeplearning.ai"?

Thanks,
Arthur Chan​

https://www.facebook.com/groups/DeepLearningAISpecialization/

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