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- Summary
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Classroom Contents
Introduction to Deep Learning
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- 1 - Introduction
- 2 - Course information
- 3 - Why deep learning?
- 4 - The perceptron
- 5 - Activation functions
- 6 - Perceptron example
- 7 - From perceptrons to neural networks
- 8 - Applying neural networks
- 9 - Loss functions
- 10 - Training and gradient descent
- 11 - Backpropagation
- 12 - Setting the learning rate
- 13 - Batched gradient descent
- 14 - Regularization: dropout and early stopping
- 15 - Summary
