In this post we’ll take closer look at the Python type annotations and some immediate benefits that come with using them. First part will give you some overview of the type annotation basics and second part will show you how to leverage them to speed up your code and create a basic web service to access the library in just a few lines of code.

Locality-sensitive hashing (LSH) allows for fast retrieval of similar objects from an index - orders of magnitude faster than simple search at the cost of some additional computation and some false positives/negatives. In the last post I introduced LSH for angular distance. In this one I will tell you how you can fine-tune it to get the expected results.

Locality-sensitive hashing (LSH) is an important group of techniques which can be used to speed up vastly the task of finding similar sets or vectors.

In the previous posts I wrote about the finite-state automata (FSA). Now we’ll cover finite-state transducers (FST), which allow to index text with values in libraries such as elasticsearch.

In this post we’ll take closer look at the Python implementation of algorithm for constructing finite-state automata from unsorted set of words.

Have you ever wondered how Lucene/Elasticsearch does its job so well? This post will teach you about essential part of the Lucene index - minimal finite-state automaton (FSA).

Ensembling is a ML technique in which we use multiple learning algorithms to get better performance than could be obtained from any of the algorithms alone.

Learn how to set maximum number of system resources that can be allocated to running docker processes and containers in Ubuntu