Tools and Techniques
The team at Google Design developed these seven key steps for staying user-focused when designing ML-driven products. Rule #1: "Don’t expect Machine learning to figure out what problems to solve." That may sound obvious but there are a lot of key insights throughout this article.
It's common to acquire technical debt when building software and generally, that's okay as long as it's not ignored. But with machine learning systems, technical debt can accumulate very quickly and can kill a fast-moving project. Here's why that happens and what to watch out for on your own projects.
There are many applications for reducing the dimensionality of large datasets. In this post, Elior Cohen explores three commonly used dimensionality reduction techniques: PCA, t-SNE and Auto Encoders. For each, Elior describes how they work and considerations for when to use them. This is easy to follow and includes lots of code snippets and diagrams.
Nice tutorial by Brandon Rose that offers a strategy for making sense of massive amounts of unstructured text. This is easy to follow and includes code.