Lecture

Feed-forward Networks

In course

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Description

This lecture covers the fundamentals of feed-forward networks, including the structure of neural networks, training goals, activation functions, gradient descent optimization, backpropagation, and the challenges of vanishing and exploding gradients. It also explores the application of feed-forward networks in time series forecasting, volatility forecasting, and bond return predictability using machine learning techniques.

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Official source

https://mediaspace.epfl.ch/media/0_plow2bmq

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

Computer engineering

Computer graphics: 2D computer graphics

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