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Hi all, Jeremy Howard here. I'm the instructor for this MOOC - feel free to ask any questions here.
by jph00 10y ago
Hi all, Jeremy Howard here. I'm the instructor for this MOOC - feel free to ask any questions here.
- rsp1984 10y agoWill this offer stay free?
- jph00 10y agoYes. Our goal is to democratize deep learning, not to make money :)
- cr0sh 10y agoWill it be available in the future - that is, can I take the course later (I'm currently involved with the Udacity Self-Driving Car Engineer nanodegree - and that will finish at the end of July roughly - I have no free time for anything else right now)?
- throwaway2016a 10y agoI know it says graduate level math not required but it does need some math correct? What is the minimum?
- jph00 10y agoYou need to have a basic understanding of matrix products (from linear algebra) and the chain rule (from calculus). Both can be learnt on demand using Khan Academy - no need to go study math first, but just study what you need when you need it. We give brief explanations of both when they come up. The wiki (http://wiki.fast.ai http://wiki.fast.ai) has links to necessary learning resources for each lesson.
- bogomipz 10y agoSo these are the two subsets of those maths needed for this course but in general is Deep Learning largely based in just Calculus and Linear Algebra? Are there any others you would recommend learning and/or brushing up on in a more general sense for ML/DL? Thanks for making this available by the way!
- jph00 10y agoDeep learning is pretty much entirely linear algebra and calculus, at a mathematical level. But I really think that a code-first approach is much better. So I'd suggest just getting started working through the course, and really dive into the Jupyter notebooks for each lesson. Do lots of experiments.
- bogomipz 10y ago>"But I really think that a code-first approach is much better." This is really refreshing to hear Most of the books I have looked at seem to be mostly math-first and very densely so at that. As much as I appreciate math it's not always the warmest welcome. I am looking forward to working through your course and seeing part 2 as well. Cheers.
- Cybiote 10y agoIf you want a solid understanding of DL⊂ML, you'll also need a good understanding of probability theory and information theory. While the mechanics require just matrix math and some vector calc, you'll also want to know linear algebra proper and these days, function spaces especially. A small helping of analysis, for measure theory, will not hurt either.
- bogomipz 10y agoInteresting that you mentioned Information Theory, when I think of this I think of Shannon, encoding symbols, bauds, bit, noise and channels in the Telecom network sense. But I guess this all applies quite similarly in the neural network sense in DL/ML as well?
- deepnotderp 10y agoHow the hell is deep learning graduate level math?
- jph00 10y agoSome courses are, eg https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/ https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni... .
- deepnotderp 10y agoThere's no way that's graduate level math. I've gone through Nando's lectures and although they're more mathematically involved than most courses, they aren't grad level math.
- jph00 10y agoIIRC the course details suggest it is most appropriate for a graduate level. I may be misremembering however.
- master_yoda_1 10y agoYou might be right. The level of education is different for every individual and also people come from different country. In America you can be a graduate in computer science without knowing calculus, and in some other country they teach calculus/probability in high school. So the above person might have studied the chain rule and entropy in high school. So its not exactly graduate level math for everyone.
- vinay427 10y ago> In America you can be a graduate in computer science without knowing calculus, and in some other country they teach calculus/probability in high school. This certainly isn't true at my university in the US. As a matter of fact, I'm not sure why you named a country at all seeing as this varies by university. Anyway, I would definitely agree that on the scale of reputable international universities this is not grad-level math. A graduate school in a well-qualified university would expect students to either know this material or be able to learn it on their own. They are intro classes for the math major at my (and many other) universities.
- hanman9 10y agoThanks, Jeremy! My background is in chip design verification; Looks like a great starting point for me to dabble in Deep learning.
- dharma1 10y agoLooks good! Recommendations on how to go from basics (being able to fine-tune pretrained ImageNet/Inceptionv3 with new data etc) to a real project? I'd like to play with semantic segmentation of satellite images (hyperspectral). Any pointers?
- jph00 10y agoThat's really what this course tries to do. Lesson 7 shows how to do localization in a couple of different ways. And in every lesson I try to show how to get a state of the art result on a real dataset, showing the process from end to end. Jupyter notebooks are available for all of these projects. (We'll be looking at more segmentation techniques in the next part of the course next year.)
- kirkules 10y agoJust FYI: The AWS console UI is currently different in a number of ways from the interface shown in the "how to set up your aws deep learning server" video, beyond what is accounted for in the overlay-text amendments to the video. (e.g. creating a user has 4 involved steps before you get to see the security credentials, including setting up a user group and access policies for that group -- I made my user have the AdministratorAccess policy...)
- loco5niner 10y ago* I would amend the above comment to say that setting up a user group is not required (although it is not exactly clear that it is not).
- jph00 10y agosigh I wish AWS didn't make this bit so complex. And that they stopped changing it! Thanks for letting us know.