Tag Archives: Julia

Calling Python’s scikit-learn machine learning library from Julia

It turns out to be very easy. Here’s a python code for SVM classification And here’s Julia code – utilizing PyCall module


Linear Algebra in 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 […]


Neural networks and a dive into Julia

via ŷhat | Neural networks and a dive into Julia.

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.

IJavascript: A Javascript Kernel for IPython’s Graphical Notebook

via n-riesco/ijavascript · GitHub.

IJavascript is an 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.

The execution of code is carried out by means of a kernel that implements the IPython messaging protocol. There are kernels available for Python, Julia, Ruby, Haskell and many others.

IJavascript implements the latest stable specification of the protocol, version 4.1. This specification will be updated to version 5.0 in the next release of IPython.

A repository of IPython notebooks can be found here.

The Polyglot Beaker Notebook

View slides for this presentation here:

PyData NYC 2014
The Beaker Notebook is a new open source tool for collaborative data science. Like IPython, Beaker uses a notebook-based metaphor for idea flow. However, Beaker was designed to be polyglot from the ground up. That is, a single notebook may contain cells from multiple different languages that communicate with one another through a unique feature called autotranslation. You can set a variable in a Python cell and then read that variable in a subsequent R cell, and everything just works – magically. Beaker comes with built-in support for Python, R, Groovy, Julia, and Javascript. In addition, Beaker also supports multiple kinds of cells for text, like HTML, LaTeX, Markdown, and our own visualization library that allows for the plotting of large data sets. This talk will motivate the design, review the architecture, and include a live demo of Beaker in action.