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:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://ericyoc/the_applied_ai_universe_coding_guide") - Notebooks
- Google Colab
- Kaggle
| cff-version: 1.2.0 | |
| message: "If you use these artifacts, please cite the book they accompany." | |
| type: dataset | |
| title: "The Applied AI Universe Coding Guide - model artifacts" | |
| abstract: >- | |
| Trained model artifacts accompanying Book 1 of The Adaptive AI Codex Series. | |
| Every artifact was produced by executed code published with the book. | |
| authors: | |
| - family-names: Yocam | |
| given-names: Eric | |
| name-suffix: "PhD, DBA" | |
| website: "https://ericyocam.com" | |
| version: "1.0.0" | |
| date-released: "2025-01-01" | |
| license: MIT | |
| url: "https://ericyocam.com/book1.html" | |
| repository-code: "https://github.com/ericyoc" | |
| keywords: | |
| - adversarial machine learning | |
| - AI security | |
| - adversarial robustness | |
| identifiers: | |
| - type: url | |
| value: "https://www.amazon.com/dp/B0H3J4V7FS" | |
| description: Amazon listing | |
| preferred-citation: | |
| type: book | |
| title: "The Applied AI Universe Coding Guide" | |
| year: 2025 | |
| publisher: | |
| name: Independently Published | |
| authors: | |
| - family-names: Yocam | |
| given-names: Eric | |
| name-suffix: "PhD, DBA" | |