baojieai

These projects are implemented in major languages: Python, Matlab/Octave, C/C++, Java, Scala, Javascript

And most of major algorithms are supports: DBN (Deep Belief Nets), RBM (Restricted Boltzmann Machine), dA (Denoising Autoencoders), SdA (Stacked Denoising Autoencoders), CNN(Convolutional Neural Networks ), and more

The 18 repos are manually selected from "100 Best GitHub: Deep Learning" http://meta-guide.com/software-meta-guide/100-best-github-de...

Each project is visualized with a project card for easy browsing

+1, must also mention oct2py, the Python to GNU Octave bridge, that runs m-files from python. Some matlab legacy m-files may be rescued in this way.

Sage and IPython are alternatives, too. http://www.sagemath.org/ http://ipython.org/

PS. I found these articles useful

Python vs Matlab http://www.pyzo.org/python_vs_matlab.html

10 Reasons Python Rocks for Research (And a Few Reasons it Doesn’t) http://www.stat.washington.edu/~hoytak/blog/whypython.html

Why Python is Better than Matlab for Scientific Software http://metarabbit.wordpress.com/2013/10/18/why-python-is-bet...

There are certainly areas Matlab rocks, and some other that Python rocks. Because of the company support, Matlab has a large scientific community and is probably easier to install (Python has Anaconda and Sage, though).

However, there is one big (maybe a killing one) weakness of Matlab: it's proprietary. I used to work in a big company and need a Matlab license. The license costs 70k$, per year! It takes months to get the purchase approved. The biggest waste in choosing Matlab is the time to get it.

And because its proprietary, we'd have to KEEP buying for YEARS, otherwise our code won't run in the next project.

So we switched to Python in later projects, which does not require bureaucratic approval chains and provides code portability