NLP

LLMs have a vast knowledge base, but training them with domain-specific data can extend their capabilities to specialized industries and tasks. This article delves into data labeling for fine-tuning and includes a step-by-step tutorial for training GPT-4o.

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.

Listening is everything—especially when it comes to effective marketing and product design. Gain key market insights from social media data using sentiment analysis and topic modeling in Python.

Colloquial language doesn’t lend itself to computation. That’s where natural language processing steps in. Learn how topic modeling helps computers understand human speech.

JavaScript frameworks based on React can help you build a fast, reliable mobile app, but it’s not always easy to determine which framework is best for your project. Choosing the wrong framework can result in an app with slow and redundant code.

JavaScript expert Sean Wang builds the same natural language processing mobile application using Cordova, Ionic, and React Native, then discusses the advantages and limitations of each.

Pre-trained models are making waves in the deep learning world. Using massive pre-training datasets, these NLP models bring previously unheard-of feats of AI within the reach of app developers.

Data collection and preparation slow down traditional NLP projects. However, transfer learning and BERT can reduce the amount of data required and change the way companies execute NLP projects.

Manually gathering information about user-generated data is time-consuming, to say the least. That’s why more organizations are turning to automatic sentiment analysis methods—but basic models don’t always cut it. In this article, Toptal Freelance Data Scientist Rudolf Eremyan gives an overview of some sentiment analysis gotchas and what can be done to address them.