[ching_wow_ka]

If you're trying to learn about deep learning, I highly suggest using Python(Theano) or Lua(Torch). They're free and used by the experts in the field for research.

Even if you don't want to use the frameworks, you'll still have access to fast linear algebra routines.

[raphaelj]

Could someone can recommend me a book about deep learning and/or machine learning for this kind of open-source library ? I do not have any background in ML nor DL.

[crazypyro]

Although this is not for beginners of machine learning (learn that first), this is a book on deep learning that is currently in pre-publication and its being written by some big names in the field.

http://www.iro.umontreal.ca/~bengioy/dlbook/

[nileshtrivedi]

NVidia has a free online course going on covering these libraries: https://developer.nvidia.com/deep-learning-courses

[obsim]

Many great material of using Torch to do machine learning and deep learning from this Oxford course: https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearni...

[wodenokoto]

Then you might actually want to start in Matlab / Octave with Mchael Ng's coursera course on ML.

[criddell]

I think you are talking about Andrew Ng's course.

I completed it and can't recommend it more highly. It is a really excellent, dense course and Ng is a very good teacher.

https://www.coursera.org/learn/machine-learning

[joshz]

Geoffrey Hinton's archived course is all about neural nets, I think you can enroll in the archived version, no code, just theory.

https://www.coursera.org/course/neuralnets

[artmageddon]

I've completed the course as well - have you used any of the knowledge from it on anything in particular after you completed the course?

[zardo]

I'm taking the Coursera course right now. The course page at Stanford has a lot of student projects. The breadth of applications is pretty huge, definitely worth a check if you're looking for an idea.

http://cs229.stanford.edu

[brudgers]

I am working through Ng's course currently. It is hitting the right tones against my mathsephobia...keeping me constantly in that state of semiunderstanding that is intuition, a term Ng uses often.

His choice of Octave/MatLab simplifies issues of dependencies. In particular the soft ones of documentation and community. This is something a lot of academic contexts get wrong with software: the tools are either to open ended and students wind up manipulating matrices with forloops or there's an inflexible stack of professional tools that require massive effort to learn and an orthogonal community or there is a toy IDE based on a senior thesis.

Octave more or less follows the Unix philosophy of doing one thing and thus can meet many people where they are rather than with a one true way.

[physicistjedi]

I think the best introductory resources are Nielsen's book [http://neuralnetworksanddeeplearning.com/] and Hinton's online course [https://www.coursera.org/course/neuralnets]. If you need something specifically for Theano, they have their own tutorial [http://deeplearning.net/tutorial/].

[rsy96]

Deep learning is pretty much a field still being rapidly advanced by research. A book on it would become obsolete the day it is published.

[temuze]

Theano is great! The learning curve can be a little difficult, but once it "clicks", it's nice to work with.