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Lecture 02: Perceptrons and Neural-Network Learning Lecture 02: Perceptrons and Neural-Network Learning
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Why start with neural networks?
Nodes, weights, and the neuron analogy
Activation: weighted sum, then threshold
Learning: update weights using prediction error
Worked example: train a perceptron to compute OR
First row: (1, 1)
Second row: (1, 0)
Third row: (0, 1)
Fourth row and final check
From a perceptron to deep learning
Backpropagation in the foundations slides