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The document summarizes two machine learning projects: 1. Face image compression using a multilayer perceptron neural network. The network takes in 16x16 pixel blocks and compresses them to lower dimensions while minimizing distortion. 2. Handwritten digit recognition using support vector machines. Images are preprocessed into 200-dimensional feature vectors capturing gradient information at different orientations. The SVM is trained on 70,000 samples and achieves 98.4% accuracy on a test set of 30,000 images.
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