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