$$\DeclareMathOperator\supp{supp} \DeclareMathOperator*{\argmax}{arg,max}$$ The Variational Autoencoder (VAE) is a not-so-new-anymore Latent Variable Model (Kingma & Welling, 2014), which by introducing a probabilistic interpretation of autoencoders, allows to not only estimate the variance/uncertainty in the predictions, but also to inject domain knowledge through the use of informative priors, and possibly to make the latent space more interpretable. VAEs have various applications: density estimation and sampling from the estimated density, i.e., data generation (e.g., image/sound/text generation and missing data imputation)[^1] semi-supervised learning representation learning for downstream tasks VAEs are generative models

Deep Learning Summit London 2019 - Day 1 === ###### tags: `RE.WORK` `Lectures` `Deep Learning` ---- # Day 1 :::info - **Date:** Sep 19, 2019 - [Link to Schedule](https://www.re-work.co/events/deep-learning-summit-london-2019/schedule) ::: --- ## Tricks for Deep Learning -BP > [name=Huma Lodhi, Data Scientist] - In industrial Deep Learning, you need to combine numerical features with boolean and categorical ones. Using _embeddings_ for the categorical ones, and simply concatenating the outpu

Deep Learning Summit London 2019 - Day 2 === ###### tags: `RE.WORK` `Lectures` `Deep Learning` # Day 2 :::info - **Date:** Sep 20, 2019 - [Link to Schedule](https://www.re-work.co/events/deep-learning-summit-london-2019/schedule) ::: --- ## Machine Learning Systems Design - UNIVERSITY OF SHEFFIELD > [name=Neil Lawrence, DeepMind Professor of Machine Learning University of Cambridge & University of Sheffield] Interesting presentation on the nuances of deploying ML systems in uncontrolled en