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_physics-based-differentiable-rendering-a-comprehensive-introduction.jpg "Physics-based rendering algorithms generate photorealistic images by simulating the flow of light through a detailed mathematical representation of a virtual scene. In contrast, physics-based differentiable rendering algorithms focus on computing derivative of images exhibiting complex light transport effects (e.g., soft shadows, interreflection, and caustics) with respect to arbitrary scene parameters such as camera pose, object geometry (e.g., vertex positions) as well as spatially varying mat")
⏲ 91 min 48 sec ✓ 07-Sep-2020
_how-to-use-edge-features-in-graph-neural-networks-and-pytorch-geometric.jpg "DeepFindr")
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_graph-neural-networks-gnn-using-pytorch-geometric-124-stanford-university.jpg "Lindsey AI")
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_cvpr-2021-tutorial-physics-based-differentiable-rendering.jpg "Physics-based rendering algorithms generate photorealistic images by simulating the flow of light through a detailed mathematical representation of a virtual scene. In contrast, physics-based differentiable rendering algorithms compute derivatives of images exhibiting complex light transport effects (e.g., soft shadows, interreflections, multiple scattering, and caustics) with respect to arbitrary scene parameters (e.g., camera pose, object geometry, spatially-varying material properties). nnThi")
⏲ 114 min 41 sec ✓ 21-Jun-2021
_gnn-project-2-creating-a-custom-dataset-in-pytorch-geometric.jpg "DeepFindr")
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_pytorch-geometric-tutorial-introduction-to-pytorch-geometric.jpg "Antonio Longa")
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_jens-agerberg-teaching-neural-networks-a-sense-of-geometry.jpg "By taking neural networks back to the school bench and teaching them some elements of geometry and topology we can build algorithms that can reason about the shape of data. Surprisingly these methods can be useful not only for computer vision – to model input data such as images or point clouds through global, robust properties – but in a wide range of applications, such as evaluating and improving the learning of embeddings, or the distribution of samples originating from generative models.")
⏲ 17 min 41 sec ✓ 19-Apr-2023
_traffic-forecasting-with-pytorch-geometric-temporal.jpg "DeepFindr")
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_pytorch-geometric-tutorial-data-handling-in-pytorch-geometric-part-1.jpg "Antonio Longa")
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_tutorial-pytorch-geometric-jianxuan-you-rex-ying.jpg "LOGML Summer School")
⏲ 1 hour 22 minutes 36 seconds 👁 748
_machine-learning-for-everybody-full-course.jpg "freeCodeCamp.org")
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_how-id-learn-ml-in-2024-if-i-could-start-over.jpg "Boris Meinardus")
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_physics-based-differentiable-rendering-a-comprehensive-introduction.webp "Physics-based rendering algorithms generate photorealistic images by simulating the flow of light through a detailed mathematical representation of a virtual scene. In contrast, physics-based differentiable rendering algorithms focus on computing derivative of images exhibiting complex light transport effects (e.g., soft shadows, interreflection, and caustics) with respect to arbitrary scene parameters such as camera pose, object geometry (e.g., vertex positions) as well as spatially varying mat")
_how-to-use-edge-features-in-graph-neural-networks-and-pytorch-geometric.webp "DeepFindr")
_graph-neural-networks-gnn-using-pytorch-geometric-124-stanford-university.webp "Lindsey AI")
_cvpr-2021-tutorial-physics-based-differentiable-rendering.webp "Physics-based rendering algorithms generate photorealistic images by simulating the flow of light through a detailed mathematical representation of a virtual scene. In contrast, physics-based differentiable rendering algorithms compute derivatives of images exhibiting complex light transport effects (e.g., soft shadows, interreflections, multiple scattering, and caustics) with respect to arbitrary scene parameters (e.g., camera pose, object geometry, spatially-varying material properties). nnThi")
_gnn-project-2-creating-a-custom-dataset-in-pytorch-geometric.webp "DeepFindr")
_pytorch-geometric-tutorial-introduction-to-pytorch-geometric.webp "Antonio Longa")
_jens-agerberg-teaching-neural-networks-a-sense-of-geometry.webp "By taking neural networks back to the school bench and teaching them some elements of geometry and topology we can build algorithms that can reason about the shape of data. Surprisingly these methods can be useful not only for computer vision – to model input data such as images or point clouds through global, robust properties – but in a wide range of applications, such as evaluating and improving the learning of embeddings, or the distribution of samples originating from generative models.")
_traffic-forecasting-with-pytorch-geometric-temporal.webp "DeepFindr")
_pytorch-geometric-tutorial-data-handling-in-pytorch-geometric-part-1.webp "Antonio Longa")
_tutorial-pytorch-geometric-jianxuan-you-rex-ying.webp "LOGML Summer School")
_machine-learning-for-everybody-full-course.webp "freeCodeCamp.org")
_how-id-learn-ml-in-2024-if-i-could-start-over.webp "Boris Meinardus")
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