Add new event (#1907)

* Add new event Signed-off-by: Chris Abraham <[email protected]> * fix speaker Signed-off-by: Chris Abraham <[email protected]> --------- Signed-off-by: Chris Abraham <[email protected]>
pytorch · Jan 31, 2025 · c98d4a1 · c98d4a1
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diff --git a/_events/multi-modal-dl-frame.md b/_events/multi-modal-dl-frame.md
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+---
+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
+
+<a href="/multi-modal-dl-frame">
+<img style="width:100%" src="/assets/images/multi-modal-dl-frame.png" alt="Multi-Modal Tabular Deep Learning with PyTorch Frame">
+</a>
+
+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
diff --git a/multi-modal-dl-frame.html b/multi-modal-dl-frame.html
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+---
+layout: default
+title: "Multi-Modal Tabular Deep Learning with PyTorch Frame"
+body-class: announcement
+background-class: announcement-background
+permalink: /multi-modal-dl-frame
+---
+
+<div class="container">
+  <div class="row hero-content">
+    <div class="col-md-10">
+      <h1>PyTorch Webinars</h1>
+    </div>
+  </div>
+</div>
+
+<div class="container-fluid light-background-section">
+  <div class="container">
+    <div class="row content">
+      <div class="col-md-10 body-side-text">
+        <img style="width:100%; max-width:600px; margin-bottom: 40px; display: block; margin-left: auto; margin-right: auto;" src="/assets/images/multi-modal-dl-frame.png" alt="AI-Powered Competitive Programming">            
+        <h2>Multi-Modal Tabular Deep Learning with PyTorch Frame</h2>
+        <p class="lead">
+          <strong>Date</strong>: February 19, 12 pm PST
+          <br/>
+          <strong>Speaker</strong>: Akihiro Nitta, Software Engineer, Kumo.ai
+          <br/>
+          <strong>Location</strong>: Online
+          <br/>
+          <br/>
+          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.
+          <br/><br/>
+          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.
+          <br/><br/>
+          <strong>Register now to attend this event.</strong>
+          <div style="width:100%;position:relative;padding-bottom:56.25%;min-height:550px;"><iframe src="https://streamyard.com/watch/wqmSrhffEigi?embed=true"  width="100%" height="100%" frameborder="0" allow="autoplay; fullscreen" style="width:100%;height:100%;position:absolute;left:0px;top:0px;overflow:hidden;"></iframe></div>
+        </p>
+      </div>
+    </div>
+  </div>
+</div>