How to convert strings to integers and floats in ClickHouse. Controlling invalid values behavior on conversion.

Managing errors when ingesting data into ClickHouse, including text data sources like CSV and TSV.

How to merge multiple large tables into a single table based on a given column. A solution to MEMORY_LIMIT_EXCEEDED problem when joining large tables.

Using regex input format can help in loading unformatted or broken text data into Clickhouse. Using Regexp format for that with a practical example.

How to use OpenAI to format unstructured text data, e.g. CSV. Setting additional formatting requirements to format specific values in the resulting CSV.

How to start using OpenAI API with Python. A simple example of a Python script that generates data based on the OpenAI language model.

How to configure Sphinx to index text data from Clickhouse. What IDs to use for Clickhouse documents with Sphinx. How to build an index and resolve found documents in Clickhouse.

How to configure multiple disks as storages in Clickhouse, and how to use different disks for different tables in Clickhouse.

The article explains what a function derivative is on a very basic level. Starting from the concept of the function, we move along function changes and finally, look at a Python example of optimizing a function based on its derivative.

Article provides an introduction to vectors and matrices, two fundamental concepts in linear algebra, which are widely used in artificial intelligence. It explains what vectors and matrices are and how they are defined in math. Basic operations with vectors and matrices using Python, including adding, multiplying, and transposing matrices.

Understanding bigram language models, which are statistical models that predict the likelihood of a word given its preceding word. Includes an example of a simple bigram language model in Python.

Basics about language models, which are algorithms that enable computers to analyze and understand human language. The article explains how language models work and how they are trained, using a simple example of a program that can understand and respond to simple questions.

Machine Learning basics, the math behind machine learning, predictions, prediction errors, training dataset, validation dataset.

Formatting CSV, TSV, and other files, converting CSV delimiters, converting CSV quoting symbols, fixing invalid CSV files, working with compressed CSV files

Reading CSV with Golang line by line or entirely, reading CSV with custom delimiters (including TSV) and escaping rules, and reading broken CSV files.

This is a welcoming post about the idea behind this place, basic approaches, target audience and goals.