Instructions to use shubhamsingh123/mnist-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use shubhamsingh123/mnist-model with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://shubhamsingh123/mnist-model") - Notebooks
- Google Colab
- Kaggle
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Download sample_data/README.md from shubhamsingh123/mnist-model: direct link, hf CLI and curl.
- Browser
- Download file 962 Bytes
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https://huggingface.co/shubhamsingh123/mnist-model/resolve/main/sample_data/README.md
- Command line
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hf download hf://shubhamsingh123/mnist-model/sample_data/README.md
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curl -L -o README.md https://huggingface.co/shubhamsingh123/mnist-model/resolve/main/sample_data/README.md
962 Bytes
This directory includes a few sample datasets to get you started.
california_housing_data*.csvis California housing data from the 1990 US Census; more information is available at: https://docs.google.com/document/d/e/2PACX-1vRhYtsvc5eOR2FWNCwaBiKL6suIOrxJig8LcSBbmCbyYsayia_DvPOOBlXZ4CAlQ5nlDD8kTaIDRwrN/pubmnist_*.csvis a small sample of the MNIST database, which is described at: http://yann.lecun.com/exdb/mnist/anscombe.jsoncontains a copy of Anscombe's quartet; it was originally described inAnscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the vega_datasets library.