Limitations of Perceptrons: What Simple Neural Models Cannot Do

Name: Limitations of Perceptrons: What Simple Neural Models Cannot Do
Uploaded: 2016-02-05T07:50:41.000Z
Duration: 14 min 35 s
Channel: Geoffrey Hinton
Description: Geoffrey Hinton elucidates the inherent limitations of perceptrons, a foundational concept in neural networks, providing critical context for the development of…

Geoffrey Hinton

University of Toronto·Geoffrey Hinton

14:35

Notebook

Type below to lock the video timestamp, or press S to capture a slide.

Limitations of Perceptrons: What Simple Neural Models Cannot Do

Geoffrey Hinton University of Toronto

14:35·2016·undergraduate·Standalone Lecture

Geoffrey Hinton elucidates the inherent limitations of perceptrons, a foundational concept in neural networks, providing critical context for the development of more complex machine learning architectures.

For cool updates on AI research, follow me

AI & ML Systems & CS Deep Learning Neural Networks & Backpropagation Perceptrons Computational Limits Toronto

About the lecturer

Speaker directory →

Geoffrey Hinton

Website Scholar

Turing Award Laureate, VP & Engineering Fellow, Google

University Professor Emeritus at Toronto receiving both the ACM Turing Award and Nobel Prize in Physics for foundational neural network architectures.

4 lectures in archiveView profile

Discussion

Share a seminar note, question, or timestamped reference (e.g. 14:20)...Sign in

Published by the lecturer

Amazon Associate

By the LecturerAmazon

Deep Learning

Ian Goodfellow, Yoshua Bengio et al.

Co-authored by Turing Laureate Yoshua Bengio—the foundational MIT Press reference on representation learning, optimization, and convolutional architectures.

MIT Press·2016·1st Edition