Neural Networks — Backpropagation, TensorFlow/Keras, CNN & Traffic Sign Classification | Harvard CS50 AI Week 5
Week 5 is about neural networks: a unit multiplies its inputs by weights, adds a bias and applies an activation function; gradient descent adjusts the weights one small step at a time, and backpropagation passes the error back so hidden layers can be trained; for images, convolution and pooling come first and an ordinary network follows, which is a CNN; for sequences, an RNN feeds its output back into itself. With time links for every section, plus the approach, common mistakes and checks for the Traffic project.