fix

Signed-off-by: Chris Abraham <[email protected]>

Author: cjyabraham

_events/pt-dinov2-multi-label-plant-species-classification.md

@@ -11,7 +11,7 @@ poster: assets/images/pt-dinov2-multi-label-plant-species-classification.png
 <img style="width:100%" src="/assets/images/pt-dinov2-multi-label-plant-species-classification.png" alt="Using PyTorch and DINOv2 for Multi-label Plant Species Classification">
 </a>
 
-Join us for an engaging webinar on our innovative transfer learning approach using self-supervised Vision Transformers (DINOv2) for multi-label plant species classification in the PlantCLEF 2024 challenge. We’ll cover how we efficiently extract feature embeddings from a dataset of 1.4 million images and utilize PyTorch Lightning for model training and Apache Spark for data management. Learn about our image processing techniques, including transforming images into grids of tiles and aggregating predictions to overcome computational challenges. Discover the significant performance improvements achieved and get insights into multi-label image classification. Perfect for PyTorch developers, this session will include a Q&A and access to our complete codebase at [github.com/dsgt-kaggle-clef/plantclef-2024].
+Join us for an engaging webinar on our innovative transfer learning approach using self-supervised Vision Transformers (DINOv2) for multi-label plant species classification in the PlantCLEF 2024 challenge. We’ll cover how we efficiently extract feature embeddings from a dataset of 1.4 million images and utilize PyTorch Lightning for model training and Apache Spark for data management. Learn about our image processing techniques, including transforming images into grids of tiles and aggregating predictions to overcome computational challenges. Discover the significant performance improvements achieved and get insights into multi-label image classification. Perfect for PyTorch developers, this session will include a Q&A and access to our complete codebase at [github.com/dsgt-kaggle-clef/plantclef-2024](https://github.com/dsgt-kaggle-clef/plantclef-2024).
 
 Murilo Gustineli is a Senior AI Software Solutions Engineer at Intel, and is currently pursuing a Master’s in Computer Science at Georgia Tech focusing on machine learning. His work involves creating synthetic datasets, fine-tuning large language models, and training multi-modal models using Intel® Gaudi® Al accelerators as part of the Development Enablement team. He is particularly interested in deep learning, information retrieval, and biodiversity research, aiming to improve species identification and support conservation efforts.
 

pt-dinov2-multi-label-plant-species-classification.html

@@ -25,13 +25,10 @@ <h2>Using PyTorch and DINOv2 for Multi-label Plant Species Classification</h2>
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           <strong>Speaker</strong>: Murilo Gustineli
           <br/>
-          <a href="https://www.youtube.com/live/zPjLHf0X78w?feature=shared">Link to session video</a>
-          <br/>
-          <a href="/assets/pytorch-frame-expert-exchange.pdf">Download slides</a>
           <br/>
           Join us for an engaging webinar on our innovative transfer learning approach using self-supervised Vision Transformers (<a href="https://dinov2.metademolab.com/">DINOv2</a>) for multi-label plant species classification in the PlantCLEF 2024 challenge. We’ll cover how we efficiently extract feature embeddings from a dataset of 1.4 million images and utilize PyTorch Lightning for model training and Apache Spark for data management. Learn about our image processing techniques, including transforming images into grids of tiles and aggregating predictions to overcome computational challenges. Discover the significant performance improvements achieved and get insights into multi-label image classification. Perfect for PyTorch developers, this session will include a Q&A and access to our complete codebase at <a href="https://github.com/dsgt-kaggle-clef/plantclef-2024">github.com/dsgt-kaggle-clef/plantclef-2024</a>.
           <br/><br/>
-          Murilo Gustineli is a Senior AI Software Solutions Engineer at Intel, and is currently pursuing a Master’s in Computer Science at <a href="https://www.gatech.edu/">Georgia Tech</a> focusing on machine learning. His work involves creating synthetic datasets, fine-tuning large language models, and training multi-modal models using Intel® Gaudi® Al accelerators as part of the Development Enablement team. He is particularly interested in deep learning, information retrieval, and biodiversity research, aiming to improve species identification and support conservation efforts. <a href="https://github.com/murilogustineli">Visit Murilo on GitHub</a>.
+          Murilo Gustineli is a Senior AI Software Solutions Engineer at <a href="https://www.intel.com/">Intel</a>, and is currently pursuing a Master’s in Computer Science at <a href="https://www.gatech.edu/">Georgia Tech</a> focusing on machine learning. His work involves creating synthetic datasets, fine-tuning large language models, and training multi-modal models using Intel® Gaudi® Al accelerators as part of the Development Enablement team. He is particularly interested in deep learning, information retrieval, and biodiversity research, aiming to improve species identification and support conservation efforts. <a href="https://github.com/murilogustineli">Visit Murilo on GitHub</a>.
           <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/n3VHFdzXfCae?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>