This seems cool, but I don't fully understand what this means. Obviously open source is good, but what are the benefits of this and does anyone have ideas on how to use it?
I can't tell if it implements any novel algorithms not available through other libraries, but it does wrap a number of separate functionalities and provide them with a nice pretty UX. It also lets you express functions symbolically so that it can quickly calculate their derivatives for the purpose of optimization problems. A lower the barrier to non-coders and a streamlined experience are pretty useful things to have.
Any recommendations for where to start with this? I'm going to give the tutorials a shot and see what happens, trying to learn Python in my free time and this seems like it would tie in nicely with that.
Oh scratch what I said before, it looks like there isn't an actual GUI associated with this library, despite the graphics on the front page... That makes the pluses of this library a bit less, as the competing packages (scikit-learn, sage, scipy, etc) already cover similar functionalities, albeit not all in one integrated system. If you are starting with Python, I would recommend first following the basic tutorials they have, then pick a basic project or task you'd like to try (i.e. digitize my handwriting), find a few examples of what approaches other people have used (i.e. RNNs / CNNs ) and work through the basics of loading your images, and training / testing whatever algorithm you choose to try.
Also holy shit the whole Virtualenv thing, which I still don't fully understand how it works beyond creating a "container" with individual installs/packages/etc. so you can better manage projects is so much easier to figure out in their instructions than on new-coder.io, where errors galore appeared while trying to figure out their instructions. Going to have to read up on RNNs and CNNs (not the tv channel?!), thanks thundara!
Virtualenv is pretty simple! Instead of installing packages to a system-wide folder, it places them locally and tells python to search that folder instead of the system's one: $ source bin/activate $ pip install pkg1 pkg2 ... $ python run_program... The underlying implementation has changed a bit over the years, but from a user's point of view, it's been extremely easy to work with.$ virtualenv .