So I was going through deeplearning.ai. You know we started a new FB group on it? We haven't public it yet but yes we are v. exited.
Now one thing you might notice of the class is that there is this optional lectures which Andrew Ng is interviewing luminaries of deep learning. Those lectures, in my view, are very different from the course lectures. Most of the topics mentioned are research and beginners would find it very perplexed. So I think these lectures deserve separate sets of notes. I still call it "quick impression" because usually I will do around 1-2 layers of literature search before I'd say I grok a video.
* Sorry I couldn't post the video because it is copyrighted by Coursera, but it should be very easy for you to find it. Of course, respect our forum rules and don't post the video here.
* This is a very interesting 40-min interview of Prof. Geoffrey Hinton. Perhaps it should also be seen as an optional material after you finish his class NNML on coursera.
* The interview is in research-level. So that means you would understand more if you took NNML or read part of Part III of deep learning.
* There are some material you heard from Prof. Hinton before, including how he became a NN/Brain researcher, how he came up with backprop and why he is not the first one who come up.
* There are also some which is new to me, like why does his and Rumelhart's paper was so influential. Oh, it has to do with his first experience on marriage relationship (Lecture 2 of NNML).
* The role of Prof. Ng in the interview is quite interesting. Andrew is also a giant in deep learning, but Prof Hinton is more the founder of the field. So you can see that Prof. Ng was trying to understand several of Prof. Hinton's thought, such as 1) Does back-propagation appear in brain? 2) The idea of capsule, which is a distributed representation of a feature vector, and allow a kind of what Hinton called "agreement". 3) Unsupervised learning such as VAE.
* On Prof. Hinton's favorite idea, and not to my surprise:
1) Boltzmann machine, 2) Stacking RBM to SBN, 3) variational method. I frankly don't fully understand Pt. 3. But then L10 to L14 of NNML are all about Pt 1 and 2. Unfortunately, not everyone love to talk about Boltzmann machine - they are not hot as GAN, and perceived as not useful at all. But if you want to understand the origin of deep learning, and one way to pre-train your DNN, you should go to take NNML.
* Prof. Hinton's advice on research is also very entertaining - he suggest you don't always read up from literature first - which according to him is good for creative researchers.
* The part I like most is Prof Hinton's view of why computer science departments are not catching up on teaching deep learning. As always, he words are penetrating. He said, " And there's a huge sea change going on, basically because our relationship to computers has changed. Instead of programming them, we now show them, and they figure it out."
* Indeed, when I first start out at work, thinking as an MLer is not regarded as cool - programming is cool. But things are changing. And we AIDL is embracing the change.