# Deep Learning: A Bayesian Perspective

## TL;DR

This paper offers a Bayesian perspective on deep learning, providing insights into optimization, regularization, and data reduction techniques, and demonstrates their application through Airbnb booking data analysis.

## Contribution

It introduces a Bayesian framework for understanding deep learning, highlighting how traditional data reduction methods relate to deep models and discussing optimization and regularization strategies.

## Key findings

- Deep layers improve data reduction and predictive performance.
- Bayesian regularization aids in weight and connection selection.
- Application to Airbnb data demonstrates practical utility.

## Abstract

Deep learning is a form of machine learning for nonlinear high dimensional pattern matching and prediction. By taking a Bayesian probabilistic perspective, we provide a number of insights into more efficient algorithms for optimisation and hyper-parameter tuning. Traditional high-dimensional data reduction techniques, such as principal component analysis (PCA), partial least squares (PLS), reduced rank regression (RRR), projection pursuit regression (PPR) are all shown to be shallow learners. Their deep learning counterparts exploit multiple deep layers of data reduction which provide predictive performance gains. Stochastic gradient descent (SGD) training optimisation and Dropout (DO) regularization provide estimation and variable selection. Bayesian regularization is central to finding weights and connections in networks to optimize the predictive bias-variance trade-off. To illustrate our methodology, we provide an analysis of international bookings on Airbnb. Finally, we conclude with directions for future research.