uoft-cs/cifar10
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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")
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.
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