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Jeremy from fast.ai here. I've posted a summary of the course materials for anyone who's interested : http://www.fast.ai/2018/05/07/part2-launch/ http://www.fas

• abcdcba 8y ago
  Hi Jeremy - For someone who doesn't like to watch lectures, will you guys ever be releasing a textbook?
  • jph00 8y ago
    I might be helping author a textbook based on our upcoming introduction to machine learning MOOC. Which maybe one day would lead to a deep learning book - but that would be years away! (BTW, I used to think I didn't like videos for learning, but actually I now think some material works best in this format. E.g. in this case we're walking through interactive notebooks, and you can follow along too. Some of the material is animated, and often I'm building up drawings as I talk. Maybe it won't be for you, but it might be worth giving it a go to see...)
    • paultopia 8y ago
      Just another thought on this, as someone slowly trying to work his way through part 1 (and generally very happy with it, thanks for this amazing resource!): videos are great when they're really short. It's tough for working people to find enough unbroken time to watch an hour or two long video, like the ones in fast.ai (as amazing as they are), especially with frequent pauses for note-taking and such. But it's much easier to handle smaller chunks. This is probably my only critique of/gripe about fast.ai---the videos pack a lot of topics in, and they could be broken at topic boundaries to make them easier to use.
      • jph00 8y ago
        Thanks for the feedback! One thing you may be interested in, if you haven't seen it, is that you can right-click the video and copy a link to the current time code, so you can always go back to where you were (or just pause the video in a tab and return later). Personally, I really hate the Coursera approach of lots of separate short videos - I totally get that some people like it, and that's fine, but it's not something I want to do myself.
        • paultopia 8y ago
          Oh, I did not know you could link to specific time slices! That's a big help, thanks!
          • Dibes 8y ago
            another thing I used a ton when going through part 1 was being signed into my google account, google kept track of my progress through the video and if I exited out and came back on a different machine it would resume where I last left off, then I would back up a few minutes to build that context back up (or attempt to anyways).
  • dominotw 8y ago
    I have the same problem. Too traumatized (when I was young) by a person teaching in a lecture format. I like self discovery and tinkering.
  • fantispug 8y ago
    I'm traditionally a textbook person too, but I really like fast.ai. You get to see how to tinker with things in real time (which is often clunky in a textbook), and there's a lot of informal folklore that people don't normally write in academic textbooks. If you're dead set against video you could just work through the notebooks yourself and read the referenced papers on the website.
• 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
• kleolon 8y ago
  Hi Jeremy. Just wanted to ask a quick question if you recommend any prerequisites before taking this course? I am a second year Computer Science student looking to get into AI, and I was just wondering if this would be a good starting point for me, or should I take some other courses and learn something else before? Thank you.
  • jph00 8y ago
    This linked course is part 2, so this isn't a good starting point. But part 1 is! :) Here's the link: http://course.fast.ai http://course.fast.ai . We assume you're familiar with python and somewhat familiar with numpy. If you're not sure if you're ready, you can always just start the first lesson and see how it goes. There are plenty of resources on the forum to help fill in specific skill gaps as they come up.
• ezhil 8y ago
  Jeremy and Rachel are very active on Twitter. I learn a lot from their tweets too! Thanks!!
• RSchaeffer 8y ago
  Hey! Thanks for these great courses and materials! How much additional math (beyond high school and introductory college courses) do these courses teach? For example, if I were to take both courses, would I be able to understand the papers published by Surya Ganguli (e.g., The Emergence of Spectral Universality in Deep Networks, Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net)?
  • poppingtonic 8y ago
    One year of Python, with high school and introductory college courses. Some basic linear algebra, familiarity with logarithms etc.
    • RSchaeffer 8y ago
      I think you misread my question. I'm asking what math students will learn, not what math students should already know.
      • bitL 8y ago
        Gram matrix, some non-linear optimization. Deep Learning is simple, complexity comes from loss function, trying to adjust weights of under-represented categories, different LEGO blocks in building your network and seeing if the particular non-linear optimization works in your case or not. You can go super deep with state-of-art math research in reading about "why do we think Deep Learning works, when it shouldn't", which is mentioned by Jeremy.
  • jph00 8y ago
    Ganguli's papers are at the more "math-y" end of the spectrum when it comes to DL papers. So don't worry if you're finding them a bit unapproachable. In part 2 of the course I provide quite a bit of advice about how to approach papers in general, and you'll get plenty of practice in reading and implementing papers - but we don't cover the specific math in this particular papers. My view is the best approach to the math in papers is to generally learn what you need as you get there. It's nearly impossible to know all the math that covers every paper you'll come across, but if you learn the meta-skill of how to learn it on demand, then you'll be just fine! :)
• gumballhead 8y ago
  Thank you for creating these courses and putting them online! I started part 1 a few weeks ago. I couldn't agree more with your teaching style. I'm someone who started college as a computer science major and couldn't finish, because it felt like I was pounding my head on the wall trying to learn abstract math when what I really wanted to do was build stuff. I finished my degree with economics, went to work in finance, and worked my way to being a developer by first doing advanced Excel, then Access, then database servers, then web development, then native development, and now more math heavy research and development. As the need arises, I keep going further down the stack and gaining more understanding. It's much more natural (for me) to learn that way.
  • geezerjay 8y ago
    > because it felt like I was pounding my head on the wall trying to learn abstract math when what I really wanted to do was build stuff. But you do need to know abstract math to build stuff, particularly if what you want to build is deep learning stuff, both models and implementations. For example, how do you expect to understand how to minimize an utility function if you have no idea of what a gradient is, how you calculate it, and why you want to descend through it.
    • jph00 8y ago
      > For example, how do you expect to understand how to minimize an utility function if you have no idea of what a gradient is, how you calculate it, and why you want to descend through it. The course teaches all those things - as the comment you're replying to states, you go deeper and deeper during the course to understand all the details. There's been a lot of research into teaching strategies that shows that this is often a more effective approach for many people than the bottom up approach widely used in math and CS. It doesn't mean that you learn any less of the foundations - just that it's in a different order.
      • geezerjay 8y ago
        > There's been a lot of research into teaching strategies that shows that this is often a more effective approach for many people than the bottom up approach widely used in math and CS. I seriously doubt that anyone can effectively learn linear algebra, multivariate calculus, optimization and regression models from an onlone tutorial on deep learning. These are subjects whose basics alone take multiple semester-long courses. If a bottom-down approach was remotely effective, no one would bother teaching the basics.
• zengid 8y ago
  Thanks Jeremy (and Rachel) for everything y'all have done; I learned a lot just from watching part 1! Somewhat tangential question, but how do you feel about sticking with PyTorch for future lessons/research work? What are your thoughts on the new Swift integration with TensorFlow?
  • jph00 8y ago
    We're so early in the software and tools cycle for DL that it's unlikely anything that exists today will remain relevant in its current form for very long. You can see that every couple of years the entire DL ecosystem is getting replaced - I don't think that will change. I'm glad you asked about Swift. It's one of the two directions I'm most excited about for the next stage of DL software (the other being javascript!) Swift is a much better language than Python, and having autograd and tensor types built in will be quite transformative. But it has nothing like the same data science library and tooling, so it'll take time to be a real option for most people. Javascript has the potential to make DL accessible even for those without a discrete GPU, and allows people to get started even without installing a tool-chain. It also allows more work to be pushed to the client - possibly even on mobile devices.
    • ScottBurson 8y ago
      Here's a vote for Julia over Swift.