Lecture

Understanding Deep Learning

In course

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Description

This lecture delves into the fundamentals of deep learning, starting with the goal of finding functions to classify images of cats and dogs using neural networks. It covers the hierarchy of features, the working principles of neural networks, and the challenges in understanding machine learning. The lecture explores the concepts of overfitting, underfitting, and the double-descent phenomenon. It also discusses the teacher-student perceptron model and the importance of sample complexity in machine learning tasks.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_09a6m2rh

About this result

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Ontological neighbourhood

Information engineering

Machine learning: Artificial neural networks

Statistics

Data analysis: Cross-validation

Statistical inference: Mathematical statistics

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