Deep learning is a form of machine learning for nonlinear high dimensional data reduction and prediction. A Bayesian probabilistic perspective provides a number of advantages. Specifically statistical interpretation and properties, more efficient algorithms for optimisation and hyper-parameter tuning, and an explanation of predictive performance. Traditional high-dimensional statistical techniques; principal component analysis (PCA), partial least squares (PLS), reduced rank regression (RRR), projection pursuit regression (PPR) are shown to be shallow learners. Their deep learning counterparts exploit multiple layers of of data reduction which leads to performance gains. Stochastic gradient descent (SGD) training and optimisation and Dropout (DO) provides model and variable selection. Bayesian regularization is central to finding networks and provides a framework for optimal bias-variance trade-off to achieve good out-of sample performance. Constructing good Bayesian predictors in high dimensions is discussed. To illustrate our methodology, we provide an analysis of first time international bookings on Airbnb. Finally, we conclude with directions for future research. Deep Learning: A Bayesian Perspective

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