Make with H2O.ai: Natural Language Processing with H2O Hydrogen Torch

Name: Make with H2O.ai: Natural Language Processing with H2O Hydrogen Torch
Uploaded: 2022-07-13
Duration: 55 min
Description: An introduction to H2O Hydrogen Torch, the no-code deep learning engine for unstructured data such as text, images, video, and audio, and why it exists.

Predictive AI
H2O Hydrogen Torch

Overview

This session introduces H2O Hydrogen Torch, H2O.ai's no-code deep learning engine for unstructured data such as text, images, video, and audio. The presenter walks through why the tool exists, to close the gap between the volume of unstructured data companies hold and the scarce deep learning talent needed to work with it, and demos several NLP problem types, including text regression, text classification, token classification, metric learning, sequence to sequence summarization, and question answering, using the platform's live experiment interface.

Key takeaways

What H2O Hydrogen Torch is: a no-code deep learning engine that trains models on your own unstructured data, text, images, video, or audio, without writing PyTorch or TensorFlow code and without sharing data externally
How to set up and run a text regression experiment end to end in the UI, including picking a pre-trained backbone model straight from Hugging Face
How text classification, token classification (entity extraction), and metric learning (duplicate question detection) work as distinct NLP problem types in the platform
How sequence to sequence models handle text summarization, and how span prediction handles question answering, both demoed live on sample datasets
How to move from a novice experience with just a few exposed hyperparameters to a master view with full hyperparameter control, including multi-GPU training and grid search

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