It turns out to be very easy. Here’s a python code for SVM classification And here’s Julia code – utilizing PyCall module
Tag Archives: Julia
Most people (including myself) are drawn to Julia by its lofty goals. Speed of C, statistical packages of R, and ease of Python?—it sounds two good to be true. However, I haven’t seen anyone who has looked into it say the developers behind the language aren’t on track to accomplish these goals. Having only been […]
Julia is an ambitious language. It aims to be as fast as C, as easy to use as Python, and as statistically inclined as R (to name a few goals). Read more about the language and why the creators are working so hard on it here: Why We Created Julia.
These claims have lead to a few heated discussions (and a few flame wars) around benchmarking along the line of “Is Julia really faster than [insert your favorite language/package here]?” I don’t think I’m the person to add to that particular conversation, but what I will say is this: Julia is fun.
A few weekends ago, I made the decision to casually brush up on my neural networks. Why? Well, for starters neural networks are super interesting. Additionally, I was keen to revisit the topic given all the activity around “deep learning” in the Twittersphere.
“There has been a great deal of hype surrounding neural networks, making them seem magical and mysterious. As we make clear in this section, they are just nonlinear statistical models”
Hastie, Tibshirani, & Friedman; Elements of Statistical Learning (2008)
Not magic, just lots of interesting (or boring depending on perspective) math.
npm package that implements a Javasript kernel for IPython’s graphical notebook (also known as Jupyter). An IPython notebook combines the creation of rich-text documents (including mathematics, plots and videos) with the execution of code in a number of programming languages.
A repository of IPython notebooks can be found here.
View slides for this presentation here:
PyData NYC 2014