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Looks pretty great, except for the fact that it would require you to learn MXNet, which hardly anybody uses. Paid for by Amazon I guess?

by m0zg 8y ago

minimaxir 8y ago
As I often say for Data Sci/AI MOOCs, it's good for learning the terminology, which will be universal regardless of framework.
maldeh 8y ago
The instructors include core contributors to MXNet. I don't imagine one would expect, say, François Chollet to do a course on Pytorch :)
- m0zg 8y ago
  Keras is quite popular as well. My point is, these courses have the cognitive load that's quite high even without having to deal with an unfamiliar framework. Don't get me wrong, it seems like a great course and anyone interested in the field will certainly get a lot of mileage out of it. But by choosing a rather obscure framework it kind of shoots itself in the foot compared to e.g. Fast.ai course, which is jam packed with practical advice and uses PyTorch. Like it or not, there are two dominant frameworks right now: TF and PyTorch, the latter is my personal favorite by far. A more practical (and fairly low-effort) approach, therefore, would be to duplicate code samples in PyTorch. That said, Fast.ai shoots itself in the foot a little too, by requiring Python 3.6 and up, which a lot of people don't have out of the box. I understand why they do it (type annotations), but still. They also hide PyTorch behind a rather large ball of Python with cognitive loads of its own.
  - maldeh 8y ago
    Your point is understood, and reiterated in other posts here. I just wanted to add context to the course as being presented by an author of MXNet, who is free to present it in his own framework. Anyway, there's no dearth of courses in the popular frameworks already, it doesn't really help enrich the community if all educational resources only taught the current "market winner", we might otherwise be missing other opportunities or ways of thinking. Imagine if all schools only taught Javascript or Java!
- mark_l_watson 8y ago
  I use Keras, TensorFlow, and PyTorch (mostly Keras) but one advantage of mxnet is that it has good Scala support and pretty good/getting better Clojure support so if you like to use those languages then mxnet could fit your work flow.
EForEndeavour 8y ago
The senior author, Alex Smola, is the director of AWS (and is a big deal: https://alex.smola.org/ https://alex.smola.org/), which helps explain the choice of Apache MXNet. That said, the authors write that "even if you use other deep learning frameworks in your research or work, we hope that these codes will help you better understand deep learning algorithms."

Looks pretty great, except for the fact that it would require you to learn MXNet, which hardly anybody uses. Paid for by Amazon I guess?

by m0zg 8y ago

Looks pretty great, except for the fact that it would require you to learn MXNet, which hardly anybody uses. Paid for by Amazon I guess?

minimaxir 8y ago

As I often say for Data Sci/AI MOOCs, it's good for learning the terminology, which will be universal regardless of framework.

maldeh 8y ago

The instructors include core contributors to MXNet. I don't imagine one would expect, say, François Chollet to do a course on Pytorch :)

m0zg 8y ago
Keras is quite popular as well. My point is, these courses have the cognitive load that's quite high even without having to deal with an unfamiliar framework. Don't get me wrong, it seems like a great course and anyone interested in the field will certainly get a lot of mileage out of it. But by choosing a rather obscure framework it kind of shoots itself in the foot compared to e.g. Fast.ai course, which is jam packed with practical advice and uses PyTorch. Like it or not, there are two dominant frameworks right now: TF and PyTorch, the latter is my personal favorite by far. A more practical (and fairly low-effort) approach, therefore, would be to duplicate code samples in PyTorch. That said, Fast.ai shoots itself in the foot a little too, by requiring Python 3.6 and up, which a lot of people don't have out of the box. I understand why they do it (type annotations), but still. They also hide PyTorch behind a rather large ball of Python with cognitive loads of its own.
- maldeh 8y ago
  Your point is understood, and reiterated in other posts here. I just wanted to add context to the course as being presented by an author of MXNet, who is free to present it in his own framework. Anyway, there's no dearth of courses in the popular frameworks already, it doesn't really help enrich the community if all educational resources only taught the current "market winner", we might otherwise be missing other opportunities or ways of thinking. Imagine if all schools only taught Javascript or Java!
mark_l_watson 8y ago
I use Keras, TensorFlow, and PyTorch (mostly Keras) but one advantage of mxnet is that it has good Scala support and pretty good/getting better Clojure support so if you like to use those languages then mxnet could fit your work flow.

EForEndeavour 8y ago

The senior author, Alex Smola, is the director of AWS (and is a big deal: https://alex.smola.org/ https://alex.smola.org/), which helps explain the choice of Apache MXNet. That said, the authors write that "even if you use other deep learning frameworks in your research or work, we hope that these codes will help you better understand deep learning algorithms."