Tabular Regression
Keras
Joblib
English
battery
state-of-health
remaining-useful-life
time-series
regression
lstm
transformer
xgboost
lightgbm
random-forest
ensemble
Instructions to use NeerajCodz/aiBatteryLifeCycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use NeerajCodz/aiBatteryLifeCycle with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://NeerajCodz/aiBatteryLifeCycle") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d75c95f76412b0608a79caba8830507564e7adf8c48a3d11c8c59632cce19b6b
- Size of remote file:
- 561 Bytes
- SHA256:
- 0ea694de9cb528f8cf481e9611642375f6c39a0f2e55a8e2215e1745bb53a88d
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