✨Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
📝 Summary:
Gradio is an open-source Python package that creates visual interfaces for ML models, making them accessible to non-specialized users via a URL. This improves collaboration by allowing easy interaction, feedback, and trust-building in interdisciplinary settings.
🔹 Publication Date: Published on Jun 6, 2019
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/1906.02569
• PDF: https://arxiv.org/pdf/1906.02569
• Github: https://github.com/gradio-app/gradio
🔹 Models citing this paper:
• https://huggingface.co/CxECHO/CE
✨ Datasets citing this paper:
• https://huggingface.co/datasets/society-ethics/papers
✨ Spaces citing this paper:
• https://huggingface.co/spaces/orYx-models/Nudge_Generator
• https://huggingface.co/spaces/society-ethics/about
• https://huggingface.co/spaces/mindmime/gradio
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#Gradio #MachineLearning #MLOps #Python #DataScience
📝 Summary:
Gradio is an open-source Python package that creates visual interfaces for ML models, making them accessible to non-specialized users via a URL. This improves collaboration by allowing easy interaction, feedback, and trust-building in interdisciplinary settings.
🔹 Publication Date: Published on Jun 6, 2019
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/1906.02569
• PDF: https://arxiv.org/pdf/1906.02569
• Github: https://github.com/gradio-app/gradio
🔹 Models citing this paper:
• https://huggingface.co/CxECHO/CE
✨ Datasets citing this paper:
• https://huggingface.co/datasets/society-ethics/papers
✨ Spaces citing this paper:
• https://huggingface.co/spaces/orYx-models/Nudge_Generator
• https://huggingface.co/spaces/society-ethics/about
• https://huggingface.co/spaces/mindmime/gradio
==================================
For more data science resources:
✓ https://xn--r1a.website/DataScienceT
#Gradio #MachineLearning #MLOps #Python #DataScience
arXiv.org
Gradio: Hassle-Free Sharing and Testing of ML Models in the Wild
Accessibility is a major challenge of machine learning (ML). Typical ML models are built by specialists and require specialized hardware/software as well as ML experience to validate. This makes...
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✨Numba-Accelerated 2D Diffusion-Limited Aggregation: Implementation and Fractal Characterization
📝 Summary:
This paper details a Numba-accelerated Python framework for 2D DLA simulations. It confirms a fractal dimension of 1.71 for dilute regimes but reveals a crossover to 1.87 compact growth in high-density environments. This provides an open-source testbed.
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15440
• PDF: https://arxiv.org/pdf/2601.15440
• Project Page: https://pypi.org/project/dla-ideal-solver/
• Github: https://github.com/sandyherho/dla-ideal-solver
==================================
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✓ https://xn--r1a.website/DataScienceT
#DLA #Fractals #ScientificComputing #Python #Simulations
📝 Summary:
This paper details a Numba-accelerated Python framework for 2D DLA simulations. It confirms a fractal dimension of 1.71 for dilute regimes but reveals a crossover to 1.87 compact growth in high-density environments. This provides an open-source testbed.
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15440
• PDF: https://arxiv.org/pdf/2601.15440
• Project Page: https://pypi.org/project/dla-ideal-solver/
• Github: https://github.com/sandyherho/dla-ideal-solver
==================================
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