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/fuzzy_controller.json from ericyoc/the_applied_ai_universe_coding_guide: direct link, hf CLI and curl.
- Browser
- Download file 199 Bytes
-
https://huggingface.co/ericyoc/the_applied_ai_universe_coding_guide/resolve/main/models/fuzzy_controller.json
- Command line
-
hf download hf://ericyoc/the_applied_ai_universe_coding_guide/models/fuzzy_controller.json
-
curl -L -o fuzzy_controller.json https://huggingface.co/ericyoc/the_applied_ai_universe_coding_guide/resolve/main/models/fuzzy_controller.json
199 Bytes
| { | |
| "centers": { | |
| "low": 20.0, | |
| "medium": 50.0, | |
| "high": 80.0 | |
| }, | |
| "width": 25.0, | |
| "actions": { | |
| "low": 0.1, | |
| "medium": 0.5, | |
| "high": 0.9 | |
| }, | |
| "note": "Mamdani fuzzy controller, Book 1 ch.1.x" | |
| } |