# A Primer on Optimal Transport #NIPS2017

DevFeed: [A Primer on Optimal Transport #NIPS2017](<https://devfeed.tech/articles/a-primer-on-optimal-transport-nips2017-40107.md>)

Original publisher: [Read original article](<https://korbonits.com/blog/2017-12-04-nips-tutorials-ot/>)

Published: 2017-12-04T08:00:00Z

Content type: tutorial

Language: en

Sources: [Alex Korbonits](<https://devfeed.tech/sources/alex-korbonits.md>)

Topics: [Tutorial](<https://devfeed.tech/topics/tutorial.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Algorithms](<https://devfeed.tech/topics/algorithms.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [NeurIPS](<https://devfeed.tech/topics/neurips.md>)

Tags: [2017](<https://devfeed.tech/tags/2017.md>), [algorithm](<https://devfeed.tech/tags/algorithm.md>), [applications](<https://devfeed.tech/tags/applications.md>), [conference](<https://devfeed.tech/tags/conference.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [ml](<https://devfeed.tech/tags/ml.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>)

## AI overview

Lecture notes on optimal transport covering the Monge problem, Kantorovich relaxation, Wasserstein distance, Sinkhorn's algorithm, and applications in generative models and machine learning.

## Source excerpt

Lecture notes from Marco Cuturi and Justin Solomon's NIPS 2017 tutorial on optimal transport -- Wasserstein distances, Sinkhorn's algorithm, and applications in generative models and ML.