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Comment by jonahx | Hacker News Reader

Hey Jeremy, thanks for making these courses! I was curious if you could offer any points of comparison between the fast.ai courses and the Udacity ML nanodegree course (or any other MOOCs you have opinions about).

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I am not Jeremy but I have taken part in the current course and also took some lessons by Siraj (who also does lessons at udacity), Andrew NG, and Karpathy. I think all of them are awesome teachers. Like Siraj's lessons, the fastai course is full of practical examples, whereas Ng/Karpathy's courses are more theoretical. What makes the fast.ai course unique compared to Siraj (who covers a lot of topics very briefly) is that Jeremy delves into depth, taking a long time explaining things, so you have a lot of opportunities to understand what's going on. In the 2018 course there is actually not that much different content compared to the 2017 course, rather Jeremy took the time and tried to make everything easier for everyone to understand and built a better python library. Also another unique point about Jeremy is that he and Rachel want not only to teach people but rather inspire a community of people working together in teams. The fast.ai library will not be just a project for the course, it will be something that a lot of people will use and contribute to to make pytorch better.

Is Rachel the lady that always keeps interrupting Jeremy so that he goes out of flow all the time, making lectures less enjoyable?

Dr Rachel Thomas, the co-founder of fast.ai, is the person who takes the highest voted questions from our 600+ in-person students and passes them on to me, as I asked her to do. At least 4 different people must have wanted the question asked before she asks it.

This approach helps ensure that if I haven't explained something clearly enough that I get another chance to do so, and, more importantly, keeps me fresh and energized throughout each lesson (I get stale and boring without some interaction).

I am a firm believer in candid feedback; I talked to multiple people taking fast.ai and all of them took it as disturbing the flow of lectures. I really like fast.ai, it's an excellent hands-on course, so please don't get offended.

Personally, I find the questions helpful, and for everyone who's watching on video, you can skip whatever portion you like, can't you?

The thing is you can visibly see how it interrupts Jeremy; he is in the middle of explaining something, then has the face of a surprised person, loses context for a few seconds etc. Better IMO would be if he just finished a small section in its entirety then did Q&A, instead of allowing himself being interrupted all the time. And often those questions are missing the point, which is to be expected with newbies, so the lecture loses efficiency. If I didn't know most of the basics already, I'd have problem following them. I found just doing the exercises better for learning.

I also find the questions very helpful. I understand for many people who already have a good understanding, these questions might be interrupting the flow. Many a times the questions his students raised were things that I had not thought of, sometimes they even stumped Jeremy. Fortunately Jeremy in most cases gives answers which vary in length, according to whether it's a concept which he will clear later, or whether it is an important concept that needs to clarified then itself.

So I think this more effective way of learning/teaching even though there is a loss in efficiency.

Rachel's mic is always off until I turn it on. So if she's asking a question, it's only after she's visually indicated that she wishes to do so, and I've found a time in my presentation that I'm ready to take it. So I'm literally never being interrupted, and can't be surprised by the fact that she's asking a question (although I may well be surprised by the content).

I do try to limit the time I wait to take a question, since I don't want to move on with a topic where I've failed to properly explain some foundational piece.

I like the questions. Some of the doubts I also have. They improve a lot the course.

Same, there have been several times I have had a question and someone thankfully asks the exact one I had. And Jeremy does a great job explaining it. It really helps to get at some of the why's behind the magic.

You are missing the point, that is full part of their experience opening to unrepresented classes and styles. It is human together with efficient, the heart added to the mind.

Since you appreciate candid feedback, I would like to explain why I down-voted you. Your post comes off as super rude and dismissive. You may have some valid criticism to give but, take more care in how you deliver it. It's a bad look for HN.

I appreciate your feedback! :) It's difficult to convey mental state over a single line of text, giving rise to various interpretations that weren't in the intent. Imagine I am your best friend and I say the same in a playful fashion - would that be more acceptable?

Absolutely! That's actually in the hacker news commenting guidelines: "Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith." https://news.ycombinator.com/newsguidelines.html

So to answer you question, yes, it would be more acceptable if you were saying that to me in person. In fact, I agree with you about the flow of the lectures, and I was looking for someone to bring that up and I'm glad that it's getting discussed here.

However, Jeremy and Rachel are both reading these comments and we should strive to provide thoughtful, fleshed out feedback. The work they've done on Fast.ai is a tremendous lift, and deserves more than a drive-by comment.

I haven't taken the course - and I'm hopelessly biased anyway so you shouldn't trust my comments. However a lot of our students have done it. Here's a couple of their comments (search the linked thread if you want more):

- http://forums.fast.ai/t/how-has-your-journey-been-so-far-lea...

Some folks have taken the Udacity flying car and self-driving car courses as well as fast.ai, and had success building on that combination, e.g. http://forums.fast.ai/t/meet-greet-thread-introduce-yourself...

fast.ai 1 is much better than Udacity's ML if you want to do purely Deep Learning. Udacity's DLF ND is IMO better than fast.ai 1. However, fast.ai 2 is way better than Udacity's DLF ND, and only their self-driving car has some Deep Learning models that are even more awesome (imagine driving using Deep Learning only). Take both fast.ai 1 and DLF ND if you are a beginner; after that jump on Stanford's CV & NLP courses; Oxford NLP might be a good idea as well. Then do UCL's Reinforcement Learning and you are set.

Thanks Jeremy and Rachel for the excellent materials. I am a beginner with ML and initially tried to start with fast.ai part-1. I struggled initially as I preferred to know some basics. So, I looked for a beginner friendly course and ended up with Udacity's AI programming with Python. It gives a pretty decent overview on the basics. Now, I am very excited to take up the fast.ai part-1 & part-2 courses.

I didn't take the Udacity ML Nanodegree, though I did do the Deep Learning Nanodegree.

I found it to be disappointing especially since they had hyped the collaboration with Siraj, which was nothing more than linking to certain YouTube videos.

The project feedback was sometimes helpful. I felt like most of the time though, the feedback was "you did this wrong, read this article" instead of something more personal like an elaborate explanation on why you should do things a certain way. I even once explained why I initialized a model a certain way and the reviewer ignored it when critiquing my model, which almost felt like "all students have to do it this way."

It wasn't all bad. My favorite parts were learning about GAN's with videos and a notebook from Goodfellow. And when I was trying to build more intuition about CNN's, the videos with Vincent Vanhoucke were helpful.

But altogether I felt a little disappointed in the actual projects. Maybe it was because I felt like the math was glossed over and it was too many topics with shallow exploration for a single course. I actually wished that Udacity offered a single course for say, CNN's and GAN's, going very deep into the math and processes behind them.

I'm taking another Udacity course taught by Thrun (this time, it's free) and again, he kind of glosses over why certain mathematical operations are done, at which point I spent a lot of time watching lectures by other professors who spent more time explaining it.

I think that's my biggest criticism about MOOC's in general, they can be very hand-wavy about very important concepts that underly a process. I've spent a great deal of time reading papers and course material from other colleges, writing throw away code, and watching videos from other profs in order to shore up an intuition that was simply not strongly built by the MOOC.

> Udacity offered a single course for say, CNN's and GAN's, going very deep into the math and processes behind them.

I think that's what are they going to do next with their AI school. They announced separate nanodegrees for CV, NLP and RL. However, I hope they aren't going to be such massive disappointments as AI ND term 2, where they basically didn't deliver what they promised, castrated projects and one could finish each of term 2 specializations in one weekend (i.e. removing image captioning project, removing real-world NLP project, etc.). A similar story happened with Robotics term 2, where instead of a real robot they promised you received a standard discount for NVidia TX2, and reinforcement learning was butchered from robot walking to robotic arm movement. So I am doubtful they can really live up to their promises with their current staff that keeps underdelivering. The only ND they made absolutely breathtaking, worth every penny, was Self-driving Car ND (IMO the best [not only online] course I've ever taken, and I took many from top 10 universities).

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