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This papers shows with a series of examples how probabilistic languages can be integrated into deep learning strategies. In particular shows how to model a multilayer perceptron unit (MPL) and a variational autoencoder (VAE). This papers shows with a series of examples how probabilistic languages can be integrated into deep learning strategies. In particular shows how to model a multilayer perceptron unit (MPL) and a variational autoencoder (VAE).
**Conceptual Categorisation of Probabilistic Programming Languages (PPL)** **Conceptual Categorisation of Deep Probabilistic Programming Languages (PPL)**
Probabilistic programming languages (PPLs) aim to express a probabilistic model as program. This abstraction is motivated by the idea that probabilistic models can become very difficult whereas implementing programs is usually a very standardised and thus easy to do.
As illustrated in the Figure below, deep PPLs
![Deep-PPL-overview](uploads/a444d5c55befeffb0cbea299fd6db0d5/Deep-PPL-overview.png) ![Deep-PPL-overview](uploads/a444d5c55befeffb0cbea299fd6db0d5/Deep-PPL-overview.png)
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