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

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

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

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