Lecture 2 | The Universal Approximation Theorem

Name: Lecture 2 | The Universal Approximation Theorem
Uploaded: 2019-08-31T04:18:34Z
Duration: 1 h 17 min 41 s
Channel: Carnegie Mellon University Deep Learning
Description: This lecture from Carnegie Mellon University explains the Universal Approximation Theorem and how neural networks compute boolean functions.

Carnegie Mellon University Deep Learning

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May be out of date. Uploaded 7 years ago, in an area where tools change fast.

Intermediate. Assumes working knowledge.

42K views7 years ago1 recommendation

AI and machine learning Computer science Neural networks and deep learning Math for machine learning Computation and complexity

This lecture from Carnegie Mellon University explains the Universal Approximation Theorem and how neural networks compute boolean functions.

Carnegie Mellon University Course: 11-785, Intro to Deep Learning Offering: Fall 2019 For more information, please visit: deeplearning.cs.cmu.edu/ Contents: • Neural Networks as Universal Approximators

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What people say. Recommended for its clear explanation of how artificial neurons compute boolean functions and the theory behind neural networks.

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On “‘Brains Are Amazing’ — Neuroscientists Discover L2/3 Human Neurons Can Compute the XOR Operation”

For anyone interested, this lecture explains how artificial neurons can compute boolean functions. Or Wikipedia: Universal Approximation Theorem. The novelty behind this discovery is that a single REAL neuron can compute XOR, while in Artificial Neural Networks (ANNs), you need several artificial neurons to do so. This is another of the many observed differences between ANNs and actual brains.

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