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