Neural Networks: Random Features and Kernel Regression

In course

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Description

This lecture covers the use of random features in neural networks, kernel regression, and the application of stochastic gradient descent. It explains the equivalence between neural networks and kernel regression, emphasizing the role of activation functions and hidden layers.

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Instructor

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Official source

https://mediaspace.epfl.ch/media/0_zx89h43z

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks, Topics in machine learning

Statistics

Data analysis: Regression analysis

Statistical inference: Non-parametric statistics

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