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
File size: 1,036 Bytes
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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"
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