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:
- b2e73c28447b32c2278dbdc90267e8060a455c809d097f05217b0b17ee8980be
- Size of remote file:
- 7.27 MB
- SHA256:
- 0c9aa7246ce7974c11d1e441e28b954df0ffd5a189f3692a14ccd24559294abc
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