Classification with Support Vector Machines

One of the most widely-used and robust classifiers is the support vector machine. Not only can it efficiently classify linear decision boundaries, but it can also classify non-linear boundaries and solve linearly inseparable problems. We’ll be discussing the inner workings of this classification jack-of-all-trades. We first have to review the perceptron so we can talk … Read moreClassification with Support Vector Machines

Dimensionality Reduction

Dimensionality Reduction is a powerful technique that is widely used in data analytics and data science to help visualize data, select good features, and to train models efficiently. We use dimensionality reduction to take higher-dimensional data and represent it in a lower dimension. We’ll discuss some of the most popular types of dimensionality reduction, such … Read moreDimensionality Reduction

Recurrent Neural Networks for Language Modeling

Many neural network models, such as plain artificial neural networks or convolutional neural networks, perform really well on a wide range of data sets. They’re being used in mathematics, physics, medicine, biology, zoology, finance, and many other fields. However, there is one major flaw: they require fixed-size inputs! The inputs to a plain neural network … Read moreRecurrent Neural Networks for Language Modeling

All About Autoencoders

Data compression is a big topic that’s used in computer vision, computer networks, computer architecture, and many other fields. The point of data compression is to convert our input into a smaller representation that we recreate, to a degree of quality. This smaller representation is what would be passed around, and, when anyone needed the original, they … Read moreAll About Autoencoders

Using Neural Networks for Regression: Radial Basis Function Networks

Neural Networks are very powerful models for classification tasks. But what about regression? Suppose we had a set of data points and wanted to project that trend into the future to make predictions. Regression has many applications in finance, physics, biology, and many other fields. Radial Basis Function Networks (RBF nets) are used for exactly … Read moreUsing Neural Networks for Regression: Radial Basis Function Networks

Face Recognition with Eigenfaces

Face recognition is ubiquitous in science fiction: the protagonist looks at a camera, and the camera scans his or her face to recognize the person. More formally, we can formulate face recognition as a classification task, where the inputs are images and the outputs are people’s names. We’re going to discuss a popular technique for face … Read moreFace Recognition with Eigenfaces

Clustering with Gaussian Mixture Models

Clustering is an essential part of any data analysis. Using an algorithm such as K-Means leads to hard assignments, meaning that each point is definitively assigned a cluster center. This leads to some interesting problems: what if the true clusters actually overlap? What about data that is more spread out; how do we assign clusters then? … Read moreClustering with Gaussian Mixture Models

Data Clustering with K-Means

Determining data clusters is an essential task to any data analysis and can be a very tedious task to do manually! This task is nearly impossible to do by hand in higher-dimensional spaces! Along comes machine learning to save the day! We will be discussing the K-Means clustering algorithm, the most popular flavor of clustering … Read moreData Clustering with K-Means

A Guide to Improving Deep Learning’s Performance

Although deep learning has great potential to produce fantastic results, we can’t simply leave everything to the learning algorithm! In other words, we can’t treat the model as some black-box, closed entity that can read our minds and perform the best! We have to be involved in the training and design process to make sure … Read moreA Guide to Improving Deep Learning’s Performance

Text Classification Tutorial with Naive Bayes

The challenge of text classification is to attach labels to bodies of text, e.g., tax document, medical form, etc. based on the text itself. For example, think of your spam folder in your email. How does your email provider know that a particular message is spam or “ham” (not spam)? We’ll take a look at … Read moreText Classification Tutorial with Naive Bayes