• Derivation: Ordinary Least Squares Solution and the Normal Equations

    Have you ever performed linear regression involving multiple predictor variables and run into this expression ? It’s called the OLS solution via Normal Equations. To find out where it comes from, read on!

  • Model Selection: Underfitting, Overfitting, and the Bias-Variance Tradeoff

    In machine learning and pattern recognition, there are many ways (an infinite number, really) of solving any one problem. Thus it is important to have an objective criterion for assessing the accuracy of candidate approaches and for selecting the right model for a data set at hand. In this post we’ll discuss the concepts of under- and overfitting and how these phenomena are related to the statistical quantities bias and variance. Finally, we will discuss how these concepts can be applied to select a model that will accurately generalize to novel scenarios/data sets.

  • Supplemental Proof: The Expected Value of a Squared Random Variable

    We want to show the following relationship:

  • A Gentle Introduction to Artificial Neural Networks

    Though many phenomena in the world can be well-modeled using basic linear regression or classification, there are also many interesting phenomena that are nonlinear in nature. In order to deal with nonlinear phenomena, there have been a diversity of nonlinear models developed.

  • Cutting Your Losses: Loss Functions & the Sum of Squared Errors Loss

    In this post we’ll introduce the notion of the loss function and its role in model parameter estimation. We’ll then focus in on a common loss function–the sum of squared errors (SSE) loss–and give some motivations and intuitions as to why this particular loss function works so well in practice.

  • Derivation: Derivatives for Common Neural Network Activation Functions

    When constructing Artificial Neural Network (ANN) models, one of the primary considerations is choosing activation functions for hidden and output layers that are differentiable. This is because calculating the backpropagated error signal that is used to determine ANN parameter updates requires the gradient of the activation function gradient . Three of the most commonly-used activation functions used in ANNs are the identity function, the logistic sigmoid function, and the hyperbolic tangent function. Examples of these functions and their associated gradients (derivatives in 1D) are plotted in Figure 1.

  • Derivation: Error Backpropagation & Gradient Descent for Neural Networks

    Artificial neural networks (ANNs) are a powerful class of models used for nonlinear regression and classification tasks that are motivated by biological neural computation. The general idea behind ANNs is pretty straightforward: map some input onto a desired target value using a distributed cascade of nonlinear transformations (see Figure 1). However, for many, myself included, the learning algorithm used to train ANNs can be difficult to get your head around at first. In this post I give a step-by-step walkthrough of the derivation of the gradient descent algorithm commonly used to train ANNs–aka the “backpropagation” algorithm. Along the way, I’ll also try to provide some high-level insights into the computations being performed during learning1.

    1. Though, I guess these days with autograd, who really needs to understand how the calculus for gradient descent works, amiright? (hint: that is a joke) 

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