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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 nanodegre

by jonahx 8y ago

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).

jph00 8y ago
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-learners/6480/33 http://forums.fast.ai/t/how-has-your-journey-been-so-far-lea... - http://forums.fast.ai/t/how-has-your-journey-been-so-far-learners/6480/45 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/13345 http://forums.fast.ai/t/meet-greet-thread-introduce-yourself...
syncopate 8y ago
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.
- bitL 8y ago
  Is Rachel the lady that always keeps interrupting Jeremy so that he goes out of flow all the time, making lectures less enjoyable?
  - jph00 8y ago
    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).
    - bitL 8y ago
      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.
      - ghufran_syed 8y ago
        Personally, I find the questions helpful, and for everyone who's watching on video, you can skip whatever portion you like, can't you?
        bitL 8y ago
        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.
bitL 8y ago
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.
ezhil 8y ago
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.
colmvp 8y ago
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.
- bitL 8y ago
  > 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).

by jonahx 8y ago

jph00 8y ago

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-learners/6480/33 http://forums.fast.ai/t/how-has-your-journey-been-so-far-lea... - http://forums.fast.ai/t/how-has-your-journey-been-so-far-learners/6480/45 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/13345 http://forums.fast.ai/t/meet-greet-thread-introduce-yourself...

syncopate 8y ago

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.

bitL 8y ago
Is Rachel the lady that always keeps interrupting Jeremy so that he goes out of flow all the time, making lectures less enjoyable?
- jph00 8y ago
  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).
  - bitL 8y ago
    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.
    - ghufran_syed 8y ago
      Personally, I find the questions helpful, and for everyone who's watching on video, you can skip whatever portion you like, can't you?
      - bitL 8y ago
        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.

bitL 8y ago

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.

ezhil 8y ago

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.

colmvp 8y ago

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.

bitL 8y ago
> 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).