Data inundates us like never before—how can we hope to analyze it? Graphs (networks, not bar graphs) provide an elegant approach. Find out how to start with the Python NetworkX library to describe, visualize, and analyze "graph theory" datasets.
How source code becomes a running program is often opaque: "Just run the compiler" is all that developers normally need to know.
Writing an interpreter from scratch—including its lexer and parser—is an illuminating challenge.
Indexes and partitioning can help with SQL performance, but they're not cure-alls. Through everyday examples of date range and LIKE queries, find out how to "think like an RDBMS" to make yours run faster.
Harnessing the potential of machine learning for computer vision is not a new concept but recent advances and the availability of new tools and datasets have made it more accessible to developers.
In this article, Toptal Software Developer Teimur Gasanov demonstrates how you can create an app capable of identifying handwritten digits in under 30 minutes, including the API and UI.
Data quality is a crucial element of any successful data warehouse solution. As the complexity of data warehouses increases, so does the need for data quality processes.
In this article, Toptal Data Quality Developer Alexander Hauskrecht outlines how you can ensure a high degree of data quality and why this process is so important.
The Tanagra.js library is designed to be simple and lightweight, and it currently supports Node.js and ES6 classes. The main implementation supports JSON, and an experimental version supports Google Protocol Buffers.
Retailers often face supply and demand issues that cause them to miss out on potential sales or tie up a lot of money in overstocked products.
In this article, Toptal Data Scientist Ahmed Khaled explains how retailers can boost revenues and cut costs with sales forecasts backed by artificial intelligence.