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– Backprop in practice
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Classroom Contents
Gradient Descent and the Backpropagation Algorithm
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- 1 – Supervised learning
- 2 – Parametrised models
- 3 – Block diagram
- 4 – Loss function, average loss
- 5 – Gradient descent
- 6 – Traditional neural nets
- 7 – Backprop through a non-linear function
- 8 – Backprop through a weighted sum
- 9 – PyTorch implementation
- 10 – Backprop through a functional module
- 11 – Backprop through a functional module
- 12 – Backprop in practice
- 13 – Learning representations
- 14 – Shallow networks are universal approximators!
- 15 – Multilayer architectures == compositional structure of data
