Instructions to use ramirces/anomalydetectiondataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ramirces/anomalydetectiondataset with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://ramirces/anomalydetectiondataset") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0f7411be449d65e4627bc69626a75396be5ee6dedcb789f32b5e2eb13e00c528
- Size of remote file:
- 338 kB
- SHA256:
- 12b8349fe5937e7473646bb01b62660361a49d84d5b20526e4729c15ec25b824
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