algorithm – by Matt Zhang

Adaptive Boosting (AdaBoost)

Blog of Feeling Responsive AdaBoost is a systemmatic way to construct a complicated model (strong learner) by combining many copies of a simple model (weak learner). Each simple model is fit to a reweighted data set, where unexplained data have higher weights. Read More ›

algorithm – by Alex Munoz

Fractal Compression

Blog of Feeling Responsive Fractal compression uses self-similarity in images and functions to reduce the redundant content. This technique takes redundancies and stores them as affine transformations with a set of coordinates. Read More ›

algorithm – by Benjamin Villalonga Correa

Introduction to Supervised Machine Learning

Blog of Feeling Responsive With Supervised Machine Learning techniques we can train a model to be able to recognize and classify inputs such as handritten digits, human faces, objects in a picture or sports teams with high chances of winning a game. One of the most used strategies for doing so is the use of artificial neural networks. Read More ›

algorithm – by Brian Busemeyer

Compressed sensing

Blog of Feeling Responsive Compressed sensing is a way of extracting a full signal out of a sparse sampling. It's only requirement is that the signal has a sparse representation in some basis, which is actually true for most interesting signals that we encounter. Read More ›

algorithm – by Dima Kochkov

Boltzmann Machines

Blog of Feeling Responsive Boltzmann Machines represent a class of Neural Networks that can be used for unsupervised learning. Inspired by ideas from physics and neuroscience these nets allow a simple, genuine learning rule. The learning is based on minimization of Kullback–Leibler divergence between learned probability distribution and the dataset. Read More ›