Deep Learning

Single Layer Neural Network

Example: MNIST Digits

  • Handwritten digits 28 × 28 grayscale images 60K train, 10K test images Features are the 784 pixel grayscale values \(\in\) (0, 255) Labels are the digit class 0–9

  • Goal: build a classifier to predict the image class

  • We build a two-layer network with 256 units at first layer, 128 units at second layer, and 10 units at output layer

  • Along with intercepts (called biases) there are 235,146 parameters (referred to as weights)