How to train state of the art NLP models without writing code using Ludwig

October 7th at 1:45 PM ET – 2:15 PM ET

Register – Free

The talk will introduce Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code. It is unique in its ability to help make deep learning easier to understand for non-experts and enable faster model improvement iteration cycles for experienced machine learning developers and researchers alike.

By using Ludwig, experts and researchers can simplify the prototyping process and streamline data processing so that they can focus on developing deep learning architectures.

The talk will focus on NLP applications of Ludwig, like intent classification, NLU, sentiment analysis, summarization, multi-label classification, and more.

About the speaker

Piero Molino

ML/NLP Research Scientist at Stanford University & Committer

Piero Molino is a Staff Research Scientist at Stanford University working on Machine Learning systems and algorithms. Piero completed a Ph.D. on Question Answering at the University of Bari, Italy.

Founded QuestionCube, a startup that built a framework for semantic search and QA. Worked for Yahoo Labs in Barcelona on learning to rank, IBM Watson in New York on natural language processing with deep learning and then joined Geometric Intelligence, where he worked on grounded language understanding. After Uber acquired Geometric Intelligence, he became one of the founding members of Uber AI Labs. At Uber, he worked on research topics including Dialogue Systems, Language Generation, Graph Representation Learning, Computer Vision, Reinforcement Learning, and Meta-Learning.

He also worked on several deployed systems like COTA, an ML and NLP model for Customer Support, Dialogue Systems for driver hands-free dispatch, pickup and communications, and on the Uber Eats Reommender System with graph learning. He is the author of Ludwig, a code-free deep learning toolbox.


Sessions: October 6 – 9
Trainings: October 13 – 16


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