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​ - Introduction
​ - Course information
​ - Why deep learning?
​ - The perceptron
​ - Perceptron example
​ - From perceptrons to neural networks
​ - Applying neural networks
​ - Loss functions
​ - Training and gradient descent
​ - Backpropagation
​ - Setting the learning rate
​ - Batched gradient descent
​ - Regularization: dropout and early stopping
​ - Summary
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