A Brief Introduction to Deep Learning

In this notebook, we provide a very quick (shallow?) introduction to neural networks and deep learning. We review the basic challenge of binary classification and linear decision functions and then show how features can be composed to express more complex decision surfaces. We then build a basic neural network to learn the feature functions and ultimately build more complex models for image classification.

Quick Review of Logistic Regression

We start by reviewing logistic regression. We construct a linearly separable data set and show how a logistic regression model fits this data.