- The chapter demonstrates how a neural network can learn to distinguish between images of digits 3 and 7 by itself, using a simple weight adjustment technique.
- Initially, weights are randomly initialized and produce incorrect scores; the learning process adjusts weights in the direction of the correct output.
- The adjustment is proportional to the input pixel values (bright pixels matter more) and controlled by a learning rate (e.g., 0.01).
- This simple approach, called backpropagation, achieves over 98% accuracy on clean test data, comparable to the earlier hand-configured network.
- The code initializes a random weights matrix, applies a learning rate via an identity matrix, and updates weights and bias after each training sample based on the margin of error.
- The process avoids infinite weight growth by using thresholds (2 or -2) for updates, which will later be replaced by activation functions.