Instructions to use jsebdev/apple_stock_predictor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use jsebdev/apple_stock_predictor with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://jsebdev/apple_stock_predictor") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 147f47f36bb4dc80754eb8ca9453ef742665a4d96553250dfa984436ec392128
- Size of remote file:
- 2.15 MB
- SHA256:
- a5aac0f5c87abba491d06c63247e1e41290d3426bc7f4f9f30c8b0c7f94c9180
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