A Simple Introduction to Zero Trust Networks

The biggest security breaches in history have cost companies billions of dollars. Most recently, in 2022, a security breach at T-Mobile cost them $350M in customer payouts alone. Security issues are some of the most pressing concerns in today’s ever evolving software world.  While security can be implemented in many ways, some are better thanContinue reading “A Simple Introduction to Zero Trust Networks”

What is an Encoder Decoder Model?

I recently had someone ask me what an encoder decoder model was and how an encoder decoder Long-Short Term Memory (LSTM) model is different from a regular (or stacked) LSTM. I’ve worked with many different kinds of RNNs, including LSTMs, and we’ve explored them on this blog. However, encoder decoder models are different from regularContinue reading “What is an Encoder Decoder Model?”

Accuracy, Precision, Recall, and F Score

How do you measure how well your machine learning model is doing? There are four main metrics for measuring the accuracy of a machine learning model. These metrics are accuracy, precision, recall, and F-Score (or F Score). In this post, we’ll be covering how to calculate each of these metrics and what they’re used for.Continue reading “Accuracy, Precision, Recall, and F Score”

Introduction to Machine Learning: K Nearest Neighbors (KNN)

K Nearest Neighbors or KNN is a standard Machine Learning algorithm used for classification. In KNN, we plot already labeled points with their label and then define decision boundaries based on the value of the hyperparameter “K”. Hyperparameter just means a parameter that we control and can use for tuning. “K” is used to representContinue reading “Introduction to Machine Learning: K Nearest Neighbors (KNN)”

Introduction to Machine Learning: K Means

Welcome to our fourth installment on Machine Learning. In this module we’re going to cover K-Means. K-Means is a clustering algorithm based on the hyperparameter “K” which dictates how many clusters there will be. A hyperparameter is just a parameter that we can adjust. Each cluster has a “centroid” or a central point that willContinue reading “Introduction to Machine Learning: K Means”

Intermediate Machine Learning: Principal Component Analysis (PCA)

Welcome to the third module in our Machine Learning series. So far we’ve covered Linear Regression and Logistic Regression. Just to recap, Linear Regression is the simplest implementation of continuous prediction (i.e. regression) and Logistic Regression is a version of regression that uses a softmax function to do classification. Now let’s get into something aContinue reading “Intermediate Machine Learning: Principal Component Analysis (PCA)”

Introduction to Machine Learning: Logistic Regression

Ping! Is this another spam email? Or is it legit? How does your email spam filter tell? Perhaps it uses a simple machine learning technique. In this post, we’re going to learn about what it is and how we can create a Python logistic regression program. We will cover: Machine Learning: What is Logistic Regression?Continue reading “Introduction to Machine Learning: Logistic Regression”