diff --git a/_events/multi-modal-dl-frame.md b/_events/multi-modal-dl-frame.md new file mode 100644 index 000000000000..0add0e3d9b68 --- /dev/null +++ b/_events/multi-modal-dl-frame.md @@ -0,0 +1,18 @@ +--- +category: event +title: "Multi-Modal Tabular Deep Learning with PyTorch Frame" +date: February 19 +poster: assets/images/multi-modal-dl-frame.png +--- + +**Date**: February 19, 12 pm PST + + +Multi-Modal Tabular Deep Learning with PyTorch Frame + + +In this talk, Akihiro introduces PyTorch Frame, a modular framework for multi-modal tabular deep learning. PyTorch Frame enables seamless integration with the PyTorch ecosystem, including PyTorch Geometric for graph-based message passing across relational data and Hugging Face Transformers for extracting rich text features. The talk also highlights its specialized data structures for efficiently handling sparse features, making PyTorch Frame an essential tool for modern tabular data. + +Akihiro Nitta is a software engineer on the ML team at Kumo.ai and a core contributor to PyTorch Frame and PyTorch Geometric, with prior experience as a maintainer of PyTorch Lightning. + +[Register now to join the event](/multi-modal-dl-frame) diff --git a/assets/images/multi-modal-dl-frame.png b/assets/images/multi-modal-dl-frame.png new file mode 100644 index 000000000000..9bb6b68c60c6 Binary files /dev/null and b/assets/images/multi-modal-dl-frame.png differ diff --git a/multi-modal-dl-frame.html b/multi-modal-dl-frame.html new file mode 100644 index 000000000000..f2468400cbdb --- /dev/null +++ b/multi-modal-dl-frame.html @@ -0,0 +1,41 @@ +--- +layout: default +title: "Multi-Modal Tabular Deep Learning with PyTorch Frame" +body-class: announcement +background-class: announcement-background +permalink: /multi-modal-dl-frame +--- + +
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PyTorch Webinars

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+ AI-Powered Competitive Programming +

Multi-Modal Tabular Deep Learning with PyTorch Frame

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+ Date: February 19, 12 pm PST +
+ Speaker: Akihiro Nitta, Software Engineer, Kumo.ai +
+ Location: Online +
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+ In this talk, Akihiro introduces PyTorch Frame, a modular framework for multi-modal tabular deep learning. PyTorch Frame enables seamless integration with the PyTorch ecosystem, including PyTorch Geometric for graph-based message passing across relational data and Hugging Face Transformers for extracting rich text features. The talk also highlights its specialized data structures for efficiently handling sparse features, making PyTorch Frame an essential tool for modern tabular data. +

+ Akihiro Nitta is a software engineer on the ML team at Kumo.ai and a core contributor to PyTorch Frame and PyTorch Geometric, with prior experience as a maintainer of PyTorch Lightning. +

+ Register now to attend this event. +

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