An Overview of Reinforcement Learning: Teaching Machines to Play Games

Think back to the time you first learned a skill: driving a car, playing an instrument, cooking a recipe. Let’s consider the example of playing chess. Initially, it might have seemed difficult, but, as you played more and more, it became easier to understand the game. After playing many games of chess, you are much … Read moreAn Overview of Reinforcement Learning: Teaching Machines to Play Games

Supervised Learning – Using Decision Trees to Classify Data

One challenge of neural or deep architectures is that it is difficult to determine what exactly is going on in the machine learning algorithm that makes a classifier decide how to classify inputs. This is a huge problem in deep learning: we can get fantastic classification accuracies, but we don’t really know what criteria a … Read moreSupervised Learning – Using Decision Trees to Classify Data

Advanced Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are used in all of the state-of-the-art language modeling tasks such as machine translation, document detection, sentiment analysis, and information extraction. Previously, we’ve only discussed the plain, vanilla recurrent neural network. We’ll be discussing state-of-the-art models that are used by companies like Google, Amazon, and Microsoft for language tasks. We’ll first … Read moreAdvanced Recurrent Neural Networks

Understanding Advanced Convolutional Neural Networks

Convolutional Neural Networks (CNNs) are used in all of the state-of-the-art vision tasks such as image classification, object detection and localization, and segmentation. Previously, we’ve only discussed the LeNet-5 architecture, but that hasn’t been used in practice for decades! We’ll discuss some more modern and complicated architectures such as GoogLeNet, ResNet, and DenseNet. These are … Read moreUnderstanding Advanced Convolutional Neural Networks

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