Self Driven Data Science — Issue #24
Here’s this weeks lineup of data-driven articles, stories, and resources delivered faithfully to your inbox for you to consume. Enjoy!
Working on applied machine learning problems helps you develop a pattern or process for quickly getting to good robust results. Once developed, you can use this process again and again on project after project. The more robust and developed your process, the faster you can get to reliable results. This post shares a process for working machine learning problems.
Bagging and Boosting are similar in that they are both ensemble techniques where a set of weak learners are combined to create a strong learner that obtains better performance. However, differentiating between the two techniques can be tricky. Check out this post for a concise and intuitive explanation.
Motivated by prior models simulating classic arcade gameplay using deep learning techniques, this article outlines a project that aims to accomplish a similar feat using much simpler methods. Cool project definitely worth a look.
Understanding what neural network are and how they work is no easy task. This article by a couple of self-taught engineers aims to tackle that problem by conveying their understanding in simplified form, so that any ML/AI beginners can easily start making sense of the technicalities of this technology.
Take a look at how data science and machine learning work at Instacart in this overview. By offering every customer same day delivery, keeping shoppers busy becomes a very hard problem. This post is about how the data team uses Monte Carlo simulations to balance supply and demand in a rapidly growing, high-variance marketplace.
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