DataScience

AI-driven speech recognition technology is set to reshape customer service, healthcare, and the legal sector. Explore the latest features and applications in this discussion with two leaders in the field.

Generative AI advances raise new questions around data ownership, content integrity, algorithmic bias, and more. Here, three experts at the forefront of NLP present recommendations for developing ethical generative AI solutions.

In this ask-me-anything-style Q&A, leading Toptal AI developer Joao Diogo de Oliveira fields questions from fellow engineers about resources for pivoting to ML, approaches to large language models, and the most critical future applications of AI.

Want to expand your skills amid the current surge of revolutionary language models like GPT-4? In this ask-me-anything-style tutorial, Toptal data scientist and AI engineer Daniel Pérez Rubio fields questions from fellow programmers on a wide range of machine learning, natural language processing, and artificial intelligence topics.

Explore X (formerly Twitter) data clusters to uncover user behaviors (e.g., repost and reply patterns) within online communities. This guide focuses on a politically charged data set to illustrate the process of visualizing and analyzing social data.

Python and R empower data scientists to solve problems using elegant syntactic sugar, simplifying coding and solution exploration. Each language brings its unique capabilities and approach to bear.

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.

IMDb ratings have genre bias: For example, dramas tend to score higher. Removing common feature bias and keeping unique characteristics, it’s possible to create a new, refined score based on IMDb information.

With the rise of big data and data science, storage and retrieval have become a critical pipeline component for data use and analysis. Recently, new data storage technologies have emerged. But the question is: Which one should you choose? Which one is best suited for data engineering?

In this article, Toptal Data Scientist Ken Hu compares three prominent storage technologies within the context of data engineering.