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this week's favorite
In this long-read, Sander shares his research journey into the usage of fingerprinting in combination with K-Nearest Neighbor to improve the indoor localisation performance of the Rijksmuseum app.
Below you find a set of charts demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a data scientist, machine learning or an ai expert. We made these charts for our new employees to make them AI Experts but we wanted to share them here to help the community.
This post walks you through the tools available in Ray and Amazon SageMaker RL that help you address challenges such as scale, security, iterative development, and operational cost when you use RL in production. For a primer on RL, see Amazon SageMaker RL – Managed Reinforcement Learning with Amazon SageMaker.
This article is intended to be the missing guide for what to expect in a machine learning interview. The observations in this post are born out of collective experiences interviewing for machine learning engineer and scientist positions, comprising over 90 hours of interview time across 80+ interviews at big (FAANG) companies, small (just out of Y-Combinator) companies, and everything in-between.
Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful (tasty?) models.