K-Means Clustering

Earlier we encountered K-nearest-neighbors, which was a lazy, effective, yet computationally intensive classification algorithm that categorizes a test point by comparing it to labeled training data, finding k most similar points in the training set, and adopting the highest-voted label from those k-points. Well, turns out we can do something similar for unsupervised learning problems,... Continue Reading →

Generative Adversarial Networks

I saw a talk recently from a research group at the university I work at who used a Generative Adversarial Network (GAN) to remove artifacts produced by cochlear implants from MRI images of the inner ear. They used a DCGAN, or deep convolutional GAN, to process the images. My curiosity was piqued, and I decided... Continue Reading →

K-Nearest Neighbors

So, as you can probably tell, this blog is a bit out-of-order. Any machine learning course or book will introduce KNN within +/- 2 pages of the first mention of 'classification' as it is probably the most simplistic classification algorithm there is. However, it is for exactly this reason why I am just now addressing... Continue Reading →

Multi-Layer Perceptron (MLP)/Deep Neural Networks (DNN)

Last time, we briefly introduced MNIST, and provided some performance metrics of the SLP model on classifying the digits. A subset of MNIST characters and their associated labels While I was pleasantly (and perhaps naively) surprised as to how well the SLP performed, the classification accuracy achieved (around 86%) is considered unthinkable, maybe even borderline… Continue Reading →

Intro and Single-Layer Perceptron (SLP)

The purpose of this blog is to document my journey into deep learning. Some background on myself: Engineer by training with specific interests in mechanical design, sensors, actuators, robotics, and embedded systems. After getting my Ph.D, I found that I actually had free time on weeknights and weekends, and I decided I wanted to do... Continue Reading →

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