Kaggle Learn review: there is a deep learning track and it is worth your time

Right from my undergrad days when I was starting out with machine learning to this date, my admiration for Kaggle continues to grow. In addition to being synonymous with and popularizing data science competitions, the platform has served as a launching pad and breeding ground for countless data science and machine learning practitioners around the world, including yours truly. In fact, skills I’d picked up from the platform are part of the reason that I recently got to join SocialCops, a company I’d admired for years. However, I hadn’t been on the platform in 2017 as much as I would … Continue reading Kaggle Learn review: there is a deep learning track and it is worth your time

GSoC 2016 Report – Rperform

Developer: Akash Tandon Mentors: Joshua Ulrich, Toby Dylan Hocking Official Project Link: Rperform: performance analysis of R package code This project meant to deal primarily with development of Rperform’s functionalities to allow developers to obtain potential performance impacts of a pull request (PR) without having to merge, extension of the package’s existing performance metric measurement and visualization functions, and development of a coherent user interface for developers to interact with. About Rperform Rperform is a package that allows R developers to track and visualize quantitative performance metrics of their code. It focuses on providing changes in a package’s performance metrics, … Continue reading GSoC 2016 Report – Rperform

Obtaining package performance data using Rperform

“In God we trust. All others must bring data.” – W. Edwards Deming In a previous post, I had discussed how Rperform uses the grammar of graphics approach to visualize an R package’s performance in terms of runtime and memory usage. The visualizations contribute significantly towards Rperform’s mission to allow package developers to quantify, analyze and visualize performance. However, at times you, the developer, might want to play with the data instead to perform analysis of your own. After going through this post, that is exactly what you would be able to do. Background If you are new to Rperform, consider … Continue reading Obtaining package performance data using Rperform

Rperform in Google Summer of Code 2016

Rperform had started as a GSoC 2015 project with an aim to “to provide a package with functions that make it easy for R package developers to track quantitative performance metrics of their code, over time.” Much of the functionality required for the same was implemented over the course of last summer. This included various performance visualization functions and integration with the Travis-CI workflow, among other things. The project has been accepted into the GSoC program again under the organization, R project for statistical computing. I will be working on it over the summer with my mentors, Toby Dylan Hocking … Continue reading Rperform in Google Summer of Code 2016