** Optical flow is the apparent motion of objects across multiple images or frames of video. It has many applications in computer vision including 3D structure from motion and video super resolution. In this article I illustrate one specific technique for calculating sub-pixel accurate dense optical flow by matching continuous image areas using optimization techniques over bilinear splines.**

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# Logistic Regression and Optimization Basics

**Logistic regression uses a sigmoid (logistic) function to pose binary classification as a curve fitting (regression) problem. It can be a useful technique, but more importantly it provides a good example to illustrate the basics of nonlinear optimization. I’ll show how to solve this problem iteratively with both gradient descent and Newton’s method as well as go over the Wolfe conditions that we’ll satisfy to guarantee fast convergence. These are the prerequisites for the upcoming neural network articles!**

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# Project: Auto-Soylent

**I’m taking a break from the usual lessons to discuss a side project of mine: Auto-Soylent. The idea of Soylent is to create a single shake that contains all nutrients a person needs. Auto-Soylent is an application that works with diy.soylent.me to generate recipes automatically from any given ingredients. Once the math was done, I decided to put my mouth where my money is and mix up a few batches of the computer generated recipe.**

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# Classification Validation

**It’s important to know how well your models are likely to work in the real world. In this article I’ll be discussing basic testing procedures for classification problems such as generating receiver operating characteristics and confusion matrices as well as performing cross validation. We’ll also take a look at the effect of over-fitting and under-fitting models.**

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# Decision Trees and Forests

**Decision trees make predictions by asking a series of simple questions. I’ll show you how to train one using a greedy approach, how to build decision forests to take advantage of the wisdom of crowds, and then we’ll be optimizing a rotation forest to build a fast version of one of the most accurate classifiers available.**

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# Least Squares Curve Fitting

**Today we’re talking about linear least squares curve fitting, but don’t be fooled by the name. I’ll show how to use it to fit polynomials and other functions, how to derive it, and how to calculate it efficiently using a Cholesky matrix decomposition.**Continue reading Least Squares Curve Fitting

# Naive Bayes

**Assume a distribution and conditional independence, calculate means and standard deviations, and use it to make predictions. It’s about the simplest thing that qualifies as machine learning. I’m not really a fan, but we’ve got to start somewhere, right?**Continue reading Naive Bayes

# What is Inductive Bias?

**Inductive bias is the set of assumptions a learner uses to predict results given inputs it has not yet encountered. It’s also the name of this blog. Let’s talk about swans.**

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