University of Cambridge > Talks.cam > Mathematics and Machine Learning > Introduction and goals

  Browse A-Z

Log in

Cambridge users (raven)details Other usersdetails No account?details

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

Introduction and goals

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Damon Wischik.

I will start with a broad overview of what neural networks are, and how the back propagation training algorithm works, in theory and in practice. I will describe some interesting applications, some fascinating phenomena, and some neural network architectures I will finish by discussing the role that neural networks should play in data science, and ask what might come next.

For slides and code snippets, see the talk archive.

This talk is part of the Mathematics and Machine Learning series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Inference Group DAK Group Meetings Climate Histories Interdisciplinary Seminar Series

Other talks

Part IIB Poster Presentations High-Dimensional Collocation for Lognormal Diffusion Problems Cohomology of the moduli space of curves Seminar – Why do policymakers seem to ignore your evidence? CANCELLED IN SYMPATHY WITH STRIKE Nationality, Alienage and Early International Rights Aspects of adaptive Galerkin FE for stochastic direct and inverse problems
 

© 2006-2024 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity