Comparison Perceptron and Gradient Descent Rule
{"Image DocumentName": "Comparison Perceptron and Gradient Descent Rule", "Image ImageDescription": "Perceptron learning rule guaranteed to succeed if. Training examples are linearly separable. No guarantee otherwise. Linear unit using Gradient Descent. Converges to hypothesis with minimum squared error. Given sufficiently small learning rate \uf068 Even when training data contains noise. Even when training data not linearly separable.", "Image XResolution": "37", "Image YResolution": "37", "Image ResolutionUnit": "Pixels/Centimeter", "Image Software": "SlidePlayer", "Image YCbCrPositioning": "Centered"}