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Monthly Post: Understanding text data with topic models

This is the first post of my series about understanding text data. A lot of the current NLP progress is made in predictive performance. But in practice, you often want to know what is going on in your data. You may have labels that are generated from external sources and you have to

understand how they relate to your text samples. You need to understand potential sources of leakage. All of these questions will be addressed in this series of tutorials. In this first post we will focus on topic models

Paper pick

There is a rising tide in NLP in particular but also everywhere in Deep-Learning and Artificial Intelligence which is called Multi-Task Learning! The new paper from Huggingface and Sebastian Ruder build a model that beats the state-of-the-art on several NLP tasks.

Tipps & Tricks

Practical Advice for Building Deep Neural Networks

Recommended reading

As a company heavily invested in AI, Uber aims to leverage machine learning (ML) in product development and the day-to-day management of our business. The cycle of prototyping, validating, and productionizing is central to ML innovation at Uber, and the less friction at each stage of this process, the faster Uber can innovate. To fulfill these needs, Uber developed Michelangelo PyML, a platform that enables rapid Python ML model development.

Newsletter

Monthly Post: Understanding text data with topic models

Paper pick

Tipps & Tricks

Recommended reading