Reinforcement Learning
Keras
LiteRT
PyTorch
ONNX
English
chess
deep-learning
tensorflow
self-play
mcts
Instructions to use nirajandhakal/StockZero-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use nirajandhakal/StockZero-v2 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://nirajandhakal/StockZero-v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ffb14257f715bdf333eb98489f3f0170661990316528e13d73df8f26e1cac790
- Size of remote file:
- 38.3 MB
- SHA256:
- f030e8aa8550ffac36a70362353a792c3b915209fcea305fa3de09d5d741ac3d
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