Lecture

Automatic Differentiation: BackProp revisited

In course

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Description

This lecture covers the basic ideas of automatic differentiation, focusing on the concept of reverse mode differentiation to optimize the filters of a convolutional layer by gradient descent. The process involves recording the computation tree on the forward path to apply the chain rule in reverse order, similar to backpropagation in a simple feedforward network. Examples of automatic differentiation are provided, showcasing the determination of children nodes of weight variables, backward scheduling, and the use of primitive operations.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_2rpok5vw

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Information engineering

Machine learning: Artificial neural networks

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