Demos and Tutorials
This section lists several demos that apply Concrete ML to some popular machine learning problems. They show how to build ML models that perform well under FHE constraints, and then how to perform the conversion to FHE.
Simpler tutorials that discuss only model usage and compilation are also available for built-in models and deep learning.
Titanic
Train an XGB classifier that can perform encrypted prediction for the Kaggle Titanic competition
Neural Network Fine-tuning
Fine-tune a VGG network to classify the CIFAR image data-sets and predict on encrypted data
Neural Network Splitting for SaaS deployment
Train a VGG-like CNN that classifies CIFAR10 encrypted images, and where an initial feature extractor is executed client-side
Handwritten digit classification
Train a neural network model to classify encrypted digit images from the MNIST data-set
Encrypted Image filtering
A Hugging Face space that applies a variety of image filters to encrypted images
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