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:
- 6539ef09ddaa67e2107eb546b6fbc8d40de78c3bc7e11b9658f3c5b43b6fc655
- Size of remote file:
- 86.1 MB
- SHA256:
- dd1535f732486f70ddc881f1a5d48d2ed43735e2bf932cd3d985a1c72b73cc0d
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