Comment by OmarIsmail | Hacker News Reader

Comment by OmarIsmail | Hacker News Reader

I've been coding for 20 years and professionally for almost 15 and I started watching the first video and found it to be pretty difficult to follow. I think you make a pretty common mistake that a lot of technical people do, which is take for granted how much "institutional" knowledge you have.

The topics touched on in the first 30 minutes of the video include: AWS, Jupityr Notebooks, Neural Networks, Tmux, and a few others. I understand that this is the reality of the situation today (very large up front cost of setting everything up) but it would be better to not even touch upon something like Tmux because it's not absolutely essential and just results in information overload. You can replace it with something like "I like using Tmux to save my terminal sessions, check out the wiki to learn more" instead of "here's this 3 minute tutorial on Tmux in the middle of how to use Jupyter notebooks". Very few people are smart enough to be concentrating on following what's going on with AWS/Jupyter notebooks and then pause that, process the Tmux stuff, and then context switch back to AWS/Jupyter.

There's a reason why the wiki/forums are so invaluable. There's definitely some really good information in the videos so if you guys had the time I really hope you edit the videos to an "abridged" versions that focus on only 1 topic instead of jumping around so much.

That's a great point - but I would like to add that the expectation is that people put in at least 10 hours a week. So the amount of material is designed to be what you can learn in 10 hours of study, with the support of the wiki and forums.

The lessons are not designed to stand alone, or to be watched just once straight through.

The details around setting up an effective environment are a key part of the course, not a diversion. We really want to show all the pieces of the applied deep learning puzzle.

In a more standard MOOC setting this would have been many more lessons , each one much shorter - but we felt the upside of having the live experience with people asking questions was worth the compromise.

I hope you stick with it and if you do I'd be interested to here if you still feel the same way at the end. We're doing another course next year so feedback is important. On the whole most our in-person students ended up liking the approach, although most found it more intensive than they expected. (We did regular anonymous surveys. )

Awesome stuff, but please let's not pretend that tmux is an important piece of the applied deep learning puzzle that needs to have anything to do with the intensiveness of the course.

I apologise if that's how my response came across. what I was trying to express is that I wish to show what I believe are best practices, and these tools are used throughout the course. I do not mean to suggest that tmux is critical, or that there are not other good options.

Personally I enjoy seeing how others setup and use their tools, and feel it is underappreciated in many courses; attempting to fill that gap is a little of my particular bias.

> Personally I enjoy seeing how others setup and use their tools, and feel it is underappreciated in many courses

Interesting - that explains a lot, thanks. Sorry to say, but I for one hate courses / tutorials where the tutor tries to force me to use his/her pet technology, totally unrelated to the topic at hand. I have always wondered what made them do it, and I guess they just want to help.

Still, if you want to be helpful, show me this favorite tech of yours and optionally point me to a separate resource for learning it, I might check it out sometime. But I didn't come here to learn tmux, I came for deep learning. My time is precious so please don't waste it. I would understand if you wanted to teach me basics of TensorFlow (and TensorBoard) or similar, but tmux, vi, emacs, bash,... are all outside the scope. IMHO of course. :)

My suggestion is even if you cover multiple topics, do it one at a time.

Do tmux, then notebooks, then whatever, instead of splicing one in to the other.

Mixing topics just makes the material less effective at both things and hard to follow.

You may well be right, and that's certainly how material is usually taught.

However my reading of the literature on this topic has led me to believe that the standard treatment is not the best approach for most students. I'm particularly influenced by David Perkins on this point - although I'm sure I could be applying his theories a lot better!

My collaborator, Rachel Thomas, has written about these ideas : http://www.fast.ai/2016/10/08/teaching-philosophy/ . We're learning as we go and we're spending a lot of time talking to our students to understand what's working and what's not. I hope each lesson sees some improvements...

I think you misunderstood my point:

It's okay to teach a little tmux, a little notebooks, and a little AWS each lesson, rather than the course be topic by topic. I agree with your point that it's a better way to teach material in a course, because it allows you to get people started doing things.

My point was that within a lesson, you should separate topics, because people don't handle the topic switches well when trying to listen -- it's basically just a cache/stack smash. Diversions are distracting, even when materials are interwoven in the course.

but if it's deep learning tmux is not essential, probably your users have their own preferred ways of saving sessions of which tmux might be one?

A way to preserve your session is essential so you can do experiments that involve long-running computations. They provide one. It's tmux.

Why would it matter if one of their students prefers something else besides tmux? The course isn't a comparative analysis of session managers.

I like the idea of this: blending offline and online education. I took CodePath's iOS class ("bootcamp" but for senior engineers), and I loved it. Keep up the good job!

I got so excited when I read your comment. No offense, but there are so many basic intro videos like what you're asking for. However, after that... there's nothing.. I've been looking for something to take me to the next level, and when I read "in the first 30 minutes of the video include: AWS, Jupityr Notebooks, Neural Networks, Tmux" I squeeled with joy. If anyone knows other advanced tutorials on how to design, manage, and scale up their operation into some seriously organized, efficient, and automated machine learning.. I'm dying to find out.

You should be thankful that he did not ask you to learn linear algebra or real analysis :)

sentdex (Harrison Kinsley) doing great stuff by making video tutorials of ML/DeepLearning. Do check them out.

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