Reinforcement Learning
Keras
PyTorch
machine-learning
deep-learning
neural-networks
quantum-machine-learning
pennylane
tensorflow
huggingface
educational
applied-ai-universe
Instructions to use ericyoc/the_applied_ai_universe_coding_guide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use ericyoc/the_applied_ai_universe_coding_guide with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://ericyoc/the_applied_ai_universe_coding_guide") - Notebooks
- Google Colab
- Kaggle
Download models/qsvm_kernel.npz from ericyoc/the_applied_ai_universe_coding_guide: direct link, hf CLI and curl.
- Browser
- Download file 1.04 kB
-
https://huggingface.co/ericyoc/the_applied_ai_universe_coding_guide/resolve/main/models/qsvm_kernel.npz
- Command line
-
hf download hf://ericyoc/the_applied_ai_universe_coding_guide/models/qsvm_kernel.npz
-
curl -L -o qsvm_kernel.npz https://huggingface.co/ericyoc/the_applied_ai_universe_coding_guide/resolve/main/models/qsvm_kernel.npz
1.04 kB
- Xet hash:
- 67129137cc27645a6d70298e7f5ca2a8aaca99cf0d957ac0fadd7e965fb69ce6
- Size of remote file:
- 1.04 kB
- SHA256:
- c1f768747e5a815ba3f7b5950ecc7951db40fc1407f68efd526e62153a594d49
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