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:
- bccae9ea9feeef2224c29c6cf54b7a74ffed3ef3661e98da3a8caca98f9f190e
- Size of remote file:
- 5.38 MB
- SHA256:
- 17ae16a3244e10d860ed152d16eaa491d75fce5543b3ac9cf60e2a689a45f404
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