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/expert_rules.json from ericyoc/the_applied_ai_universe_coding_guide: direct link, hf CLI and curl.
- Browser
- Download file 327 Bytes
-
https://huggingface.co/ericyoc/the_applied_ai_universe_coding_guide/resolve/main/models/expert_rules.json
- Command line
-
hf download hf://ericyoc/the_applied_ai_universe_coding_guide/models/expert_rules.json
-
curl -L -o expert_rules.json https://huggingface.co/ericyoc/the_applied_ai_universe_coding_guide/resolve/main/models/expert_rules.json
327 Bytes
| { | |
| "rules": [ | |
| { | |
| "conditions": [ | |
| "fever", | |
| "cough" | |
| ], | |
| "conclusion": "flu" | |
| }, | |
| { | |
| "conditions": [ | |
| "fever", | |
| "rash" | |
| ], | |
| "conclusion": "measles" | |
| }, | |
| { | |
| "conditions": [ | |
| "headache", | |
| "nausea" | |
| ], | |
| "conclusion": "migraine" | |
| } | |
| ], | |
| "note": "rule-based expert system, Book 1 ch.1.2" | |
| } |