This document provides an introduction to convolutional neural networks (CNNs) in 3 paragraphs: 1. It explains the principles behind CNNs including convolution, ReLU activation, and max pooling. Convolution extracts features from images using kernels, ReLU introduces non-linearity, and max pooling reduces data size and processing time. 2. It describes how CNN stacks work with a fully connected layer at the end to calculate probabilities for each label. The feature maps from CNN layers are input to the neural network and a softmax activation assigns decimal probabilities. 3. It discusses techniques for avoiding overfitting like data augmentation, dropout regularization, and transfer learning. Data augmentation artificially increases data variety, dropout removes activations during training,