Tag Archives: Kernel

Build Your own Simplified AngularJS in 200 Lines of JavaScript


via Build Your own Simplified AngularJS in 200 Lines of JavaScript – Minko Gechev’s blog.

In some cases the first approach is too big overhead. For instance, if you want to understand how the kernel works it is far too complex and slow to re-implement it. It might work to implement a light version of it (a model), which abstracts components that are not interesting for your learning purposes.

The second approach works pretty good, especially if you have previous experience with similar technologies. A proof for this is the paper I wrote – “AngularJS in Patterns”. It seems that it is a great introduction to the framework for experienced developers.

However, building something from scratch and understanding the core underlying principles is always better. The whole AngularJS framework is above 20k lines of code and parts of it are quite tricky. Very smart developers have worked with months over it and building everything from an empty file is very ambitious task. However, in order to understand the core of the framework and the main design principles we can simplify the things a little bit – we can build a “model”.

Video: 4.2.2 Kernels – Machine Learning Class 10-701


An IDA Pro Plugin for embedding an IPython Kernel


via james91b/ida_ipython · GitHub.

This is a plugin to embed an IPython kernel in IDA Pro. The Python ecosystem has amazing libraries (and communities) for scientific computing. IPython itself is great for exploratory data analysis. Using tools such as the IPython notebook make it easy to share code and explanations with rich media. IPython makes using IDA Python and interacting with IDA programmatically really fun and easy.

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.

Computing Particle Systems in Swift with Metal Kernel Functions


via Computing Particle Systems in Swift with Metal Kernel Functions.

I’ve looked at using Metal kernel functions in the past for image processing. This post looks at them for a different purpose: calculating and rendering large particle systems.

Ordinarily, you may write a particle system where the particle logic (e.g. changing position and velocity) is executed on the CPU and then rendered on the GPU. Metal allows us to pass through an array of objects (e.g. particle value objects) and act upon individual array items in parallel much like a shader acts on individual pixels in an image. In fact, a Metal compute shader can do the particle maths and the rendering in one pass.

My MetalParticles project creates 250,000 particles which are all attracted towards a single gravity well. The user can touch the screen to move the gravity well and this change is illustrated with a transient grey circle.

Most of the hard work is done in my view controller and much of this code is borrowed from my reaction diffusion application. I have an array named particles that is populated with Particle structs:

LISA 2014 Kernel Debugging Tutorial Materials


Click to Read: Those planning to attend the tutorial at LISA 2014 on kernel debugging will need to do some prep work prior to attending. This repo will also serve as a place to hold the files needed for following along during the tutorial.

Large-scale linear classification: status and challenges


Many classification methods such as kernel methods or decision trees are nonlinear approaches. However, linear methods of using a simple weight vector as the model remain to be very useful for many applications. By careful feature engineering and having data in a rich dimensional space, the performance may be competitive with that of
using a highly nonlinear classifier. Successful application areas include document classification and computational advertising (CTR prediction). In the first part of this talk, we give an overview of linear classification by introducing commonly used formulations. We discuss optimization techniques developed in our linear-classification package LIBLINEAR for fast training. The flexibility over kernel methods in selecting and employing optimization methods can be clearly seen in our discussion. In the second part of the talk, we select a few examples to demonstrate how linear classification is practically applied. They range from small to big data. The third part of the talk discusses issues in applying linear classification for big-data analytics. In our recent work on distributed linear classification, we see several challenges of this research topic. I will discuss them and hope to get your comments.