
Oxford University Machine Learning Course - jcr
https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning
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krat0sprakhar
The best part about this ML course is that all assignments are in Torch (a
deep learning framework in Lua) for which Andrej Karpathy has good things to
say on his blog[0]

> _" Brief digression. The code is written in Torch 7, which has recently
> become my favorite deep learning framework. I've only started working with
> Torch/LUA over the last few months and it hasn't been easy (I spent a good
> amount of time digging through the raw Torch code on Github and asking
> questions on their gitter to get things done), but once you get a hang of
> things it offers a lot of flexibility and speed. I've also worked with Caffe
> and Theano in the past and I believe Torch, while not perfect, gets its
> levels of abstraction and philosophy right better than others."_

[0] - [http://karpathy.github.io/2015/05/21/rnn-
effectiveness/](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)

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twelfthnight
Just to promote a heterodox(ford) opinion: while Torch is great, learning
Theano might be a better use of a budding data scientist's time because python
is used much more widely in the industry.

I think learning a new language like Lua along with Torch is probably useful
if someone is doing cutting edge neural network research.

~~~
vonnik
And to promote an even heterodoxer opinion, learning Deeplearning4j might be
an even better use because Java is the language of corporate IT, the language
of Hadoop (and therefore big data), and because Spark is based on the JVM. If
budding data scientists want their neural networks to scale, they should think
about distributed systems from day 1.
[http://deeplearning4j.org](http://deeplearning4j.org)

Full disclosure: I helped create DL4J, and it is a younger framework than both
Theano and Torch.

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spike021
What's the barrier to entry like for this?

For example, I've never been particularly great at math.

~~~
Xinnabar
Having looked at the problem sheets, probability (think distributions), linear
algebra and calculus are a must - Khan Academy is a great resource but nothing
really tests you like university level homework! There _was_ a brilliant
Prob/Stats course on iTunesU that got me through 3rd year - worth a look.

~~~
bosie
Can you share a link/name please?

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GabrielF00
Joe Blitzstein's Probability course at Harvard (Stat110) is very well
regarded.
[http://projects.iq.harvard.edu/stat110/home](http://projects.iq.harvard.edu/stat110/home)

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wahsd
I don't get it. I can't recall a single piece of learning content from Oxford
that didn't have the audio quality of a tin can phone. I sounds like an over-
compression issue, but it is just made even worse by horrible audio in what
seems like tiny little boxes that lectures are held in. Someone please point
Oxford towards a course on audio recording.

~~~
max-a
This. It's a shame that such promising lecture vids are close to inaudible.

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anuj_nm
I took Nando's ML courses at UBC 2 years ago. He's great at explaining complex
concepts in digestible chunks. He's able to show how ML theories are modeled
after natural processes well too (such as how speech recognition and image
processing work using deep learning and neural nets).

