Julien Heiduk
Model Context Protocol server with FastMCP
This tutorial explores how to create a MCP server with FastMCP and why use it.
Model Context Protocol server with FastMCPCleora part 2: How to create user embeddings?
This tutorial explores how to generate user embeddings from interaction data using Cleora, a high-performance graph embedding tool — ideal for recommendation systems and graph-based machine learning.
Cleora part 2: How to create user embeddings?Cleora part 1: Graph Embeddings
In the realm of data science, understanding relationships among entities is critical. Cleora shines in this context by efficiently modeling relationships
Cleora part 1: Graph EmbeddingsSimilar and complementary candidates generator
In this tutorial, we’ll explore how to leverage Word2Vec to find words that are similar or complementary. We’ll discuss two specific approaches - the IN-OUT approach and the OUT-OUT approach - both of which are useful for various applications, including recommender systems.
Similar and complementary candidates generatorPolars Introduction: Efficient Data Manipulation Compared to Pandas
Polars offers a faster and more memory-efficient alternative to Pandas for data manipulation tasks, particularly with large datasets, due to its use of parallel processing, lazy evaluation, and Arrow memory format, making it an ideal tool for data engineers and scientists seeking enhanced performance.
Polars Introduction: Efficient Data Manipulation Compared to Pandas