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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:
# 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
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
Three books, one model universe: build them, attack them, defend them.
| Book | Amazon | Models |
|---|---|---|
| 1 — Build The Applied AI Universe Coding Guide (this repo) | Amazon | Hub |
| 2 — Break Adversarial Attacks | Amazon | Hub |
| 3 — Defend Adversarial Defenses | Amazon | Hub |
Series overview: ericyocam.com
The Adaptive AI Codex Series
Book 1 by Eric Yocam, PhD, DBA.
| Title | Links | |
|---|---|---|
| 1 | The Applied AI Universe Coding Guide | Amazon · Site · GitHub |
| 2 | ...: Adversarial Attacks | Amazon · Site · GitHub |
| 3 | ...: Adversarial Defenses | Coming 2026 · Site · GitHub |
Series overview: https://ericyocam.com/applied-ai-universe-coding-guide.html
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