Lecture

Convolutional Networks: Motivation & Ideas

In course

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Description

This lecture covers the motivation and ideas behind convolutional networks, focusing on weight sharing, receptive fields, and post-activation. It explains the concept of convolutional layers, fully connected layers, and pooling layers, including max-pooling and average-pooling. The lecture also discusses the shift stride, index of the channel, and the role of non-linear functions in convolutional networks.

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Instructor

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Official source

https://mediaspace.epfl.ch/media/0_cnpftt4i

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Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics

Data collection: Statistical sampling

Mathematics

Analysis: Numerical analysis

Algebra: Linear algebra

Information engineering

Machine learning: Artificial neural networks

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