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
File size: 1,270 Bytes
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license: mit
library_name: keras
datasets:
- ylecun/mnist
- uoft-cs/cifar10
tags:
- adversarial-ml
- security
- adversarial-robustness
- adaptive-ai-codex
---
# The Applied AI Universe Coding Guide
Trained models and figures from Book 1 - the clean baselines that Books 2 and 3 attack and defend. Includes scikit-learn classifiers (.skops), Keras networks (.keras, loadable standalone), quantum circuit parameters (.npz), symbolic state (.json), and every figure the book generates.
## The Adaptive AI Codex Series
Book 1 by Eric Yocam, PhD, DBA.
| | Title | Links |
|---|---|---|
| 1 | The Applied AI Universe Coding Guide | [Amazon](https://www.amazon.com/dp/B0H3J4V7FS) 路 [Site](https://ericyocam.com/book1.html) 路 [GitHub](https://github.com/ericyoc/the_applied_ai_universe_coding_guide) |
| 2 | ...: Adversarial Attacks | [Amazon](https://www.amazon.com/dp/B0H9XH9B9J) 路 [Site](https://ericyocam.com/book2.html) 路 [GitHub](https://github.com/ericyoc/the_applied_ai_universe_adversarial_attacks) |
| 3 | ...: Adversarial Defenses | Coming 2026 路 [Site](https://ericyocam.com/book3.html) 路 [GitHub](https://github.com/ericyoc/the_applied_ai_universe_adversarial_defenses) |
Series overview: https://ericyocam.com/applied-ai-universe-coding-guide.html
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