# Deep Probabilistic Modelling with Gaussian Processes #NIPS2017

DevFeed: [Deep Probabilistic Modelling with Gaussian Processes #NIPS2017](<https://devfeed.tech/articles/deep-probabilistic-modelling-with-gaussian-processes-nips2017-40106.md>)

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

Published: 2017-12-04T12:00:00Z

Content type: tutorial

Language: en

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

Topics: [Tutorial](<https://devfeed.tech/topics/tutorial.md>), [VAE](<https://devfeed.tech/topics/vae.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [NeurIPS](<https://devfeed.tech/topics/neurips.md>)

Tags: [analysis](<https://devfeed.tech/tags/analysis.md>), [bias](<https://devfeed.tech/tags/bias.md>), [conference](<https://devfeed.tech/tags/conference.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [gaussian](<https://devfeed.tech/tags/gaussian.md>), [inference](<https://devfeed.tech/tags/inference.md>), [modelling](<https://devfeed.tech/tags/modelling.md>), [neural-network](<https://devfeed.tech/tags/neural-network.md>), [neurips](<https://devfeed.tech/tags/neurips.md>), [probabilistic](<https://devfeed.tech/tags/probabilistic.md>), [research](<https://devfeed.tech/tags/research.md>), [supervised-learning](<https://devfeed.tech/tags/supervised-learning.md>), [theory](<https://devfeed.tech/tags/theory.md>), [tutorial](<https://devfeed.tech/tags/tutorial.md>), [unsupervised-learning](<https://devfeed.tech/tags/unsupervised-learning.md>), [videos](<https://devfeed.tech/tags/videos.md>)

## AI overview

Lecture notes from a NeurIPS 2017 tutorial introduce deep probabilistic modelling with Gaussian processes, covering probabilistic neural networks, uncertainty, graphical models, and the computational challenge of inference.

## Source excerpt

Lecture notes from Neil Lawrence's NIPS 2017 tutorial on deep probabilistic modelling with Gaussian processes -- from GPs to deep GPs and variational inference.