Graph-Based Image Segmentation in Python

In this article, an implementation of an efficient graph-based image segmentation technique will be described, this algorithm was proposed by Felzenszwalb et. al. from MIT. The slides on this paper can be found from Stanford Vision Lab.. The algorithm is closely related to Kruskal’s algorithm for constructing a minimum spanning tree of a graph, as stated by the author and hence can be implemented to run in O(m log m) time, where m is the number of edges in the graph.

Experts’ Favorite Data Science Techniques

What are the most favorite techniques of the professional data scientists interviewed in DataFramed, a DataCamp podcast? Explore all 6 of them in this tutorial!
• Scatter plots
• Decision trees
• Linear regression
• Using log axes
• Logistic regression
• Principal Component Analysis (PCA)

How to use the Python debugger

This article is not about machine learning, but about a piece of software engineering that often comes handy in data science practice. When writing code, everybody gets errors. Sometimes it is difficult to debug them. Using a debugger may help, but can also be intimidating. This is a TLDR tutorial in using pdb in IPython, focused on looking at variables inside functions.

Introduction to Functional Programming in Python

Most of us have been introduced to Python as an object-oriented language; a language exclusively using classes to build our programs. While classes, and objects, are easy to start working with, there are other ways to write your Python code. Languages like Java can make it hard to move away from object-oriented thinking, but Python makes it easy.

Unleashing the potential of reinforcement learning

In this episode of the Data Show, I spoke with Danny Lange, VP of AI and machine learning at Unity Technologies. Lange previously led data and machine learning teams at Microsoft, Amazon, and Uber, where his teams were responsible for building data science tools used by other developers and analysts within those companies. When I first heard that he was moving to Unity, I was curious as to why he decided to join a company whose core product targets game developers.

Criminal goings-on in a random forest

In this blog post, we’ll use supervised machine learning to see how well we can predict crime in London. Perhaps not specific crimes. But we can use recorded crime summary data at London borough-level, with some degree of accuracy, to predict crime counts by type and location.

The R Consortium has funded half a million dollars to R projects

The R Consortium passed a significant milestone this month: since its inception, the non-profit body has provided more than US$500,000 in grant funding to project proposed by the R Community. The R Consortium uses the dues from its member organizations to fund grant proposals, which are reviewed twice a year by its Infrastructure Steering Committee. (If you’d like to propose a project, proposals for the next round are being accepted through April 1.)

Adding logging to a shiny app with loggit

This is a very short post with example code Over time when you move your shiny app from your computer to a server, you want to add some logging. Generally logging is defined in levels : INFO (everything you want to print), WARNING (it does not stop the application, but it could be a problem), and ERROR (fatal things). Shiny server does already log all it’s actions to a file on the server, but that file can be hard to access. It would be nice if every app has its own logging, close to the app location. The package loggit by Ryan J. Price, overwrites the normal message, warning and stop functions in R and replaces them with identically functioning functions, but the package ALSO writes to a file. Thus the loggit packages writes to a json file of your choosing and has nice timestamped data, which is extremely easy to parse.