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## Posts

## Simulating a Biological Neuron Using the Hodgkin-Huxley Model

To understand the Hodgkin-Huxley model, we must first understand what a neuron is. A neuron is a type of cell that is most prominently found in nerves and the brain, and neurons the primary building blocks of the nervous system. Neurons are connected by synapses, which allow signals to be sent and received rapidly and precisely. ** Read more...**

## Learning to Debug

*Note to reader: this post was written before students had access to VS Code and its associated debugging capabilities. Consequently, some valuable debugging tools like breakpoints are not covered in the post.* ** Read more...**

## Efficiently Computing the Determinant of a Matrix, Part 2: Determinant by Elementary Row Operations

*Note: This post is part 2 of a 2-part series: part 1, part 2.* ** Read more...**

## Efficiently Computing the Determinant of a Matrix, Part 1: Determinant by Cofactors

*Note: This post is part 1 of a 2-part series: part 1, part 2.* ** Read more...**

## Depth-First and Breadth-First Search

To understand what depth-first and breadth-first search are, we must first know what graphs and directed graphs are. A **graph** is a set of objects (often called nodes) that are connected to each other. A pair of nodes is called an **edge**, and edges make up the “structure” of a graph. Put simply, a graph is a set of nodes and edges. A **directed graph** is a special instance of a graph. The difference is that in a regular (undirected) graph, the edges are bidirectional, meaning that they don’t have a specific direction. However, in a directed graph, each edge has a specified direction. ** Read more...**

## Debugging 101

*Note to reader: this post was written before students had access to VS Code and its associated debugging capabilities. Consequently, some valuable debugging tools like breakpoints are not covered in the post.* ** Read more...**

## The Ultimate High School Computer Science Sequence: 9 Months In

*The Eurisko sequence started during the summer of 2020 with an initial cohort of 5 high school students, all aged 15-16 years old and entering their junior year (11th grade). The content of this sequence similar to what would be covered in upper-level undergraduate courses (e.g. data structures/algorithms ranging from linked lists & sorting algorithms to graphs & traversals), and some content may even be beyond (e.g. building a machine learning library in Python from the ground up). The students build everything from scratch: for example, instead of using external libraries like numpy or pandas, the students built their regressors and classifiers on top of matrix and dataframe classes that they wrote themselves.* ** Read more...**

## Solving Magic Squares Using Backtracking

A magic square can be thought of as a matrix with specific rows, columns, and diagonals adding up to the same number, called the magic constant. For an $n \times n$ magic square, the magic constant is ** Read more...**

## Predator-Prey Modeling with Euler Estimation

Predator-prey relationships are common in nature. One species benefits from a high population of the other, while the other species is hurt by a high population of the first species. ** Read more...**

## Linear and Logistic Regression, Part 3: Categorical Variables, Interaction Terms, and Nonlinear Transformations of Variables

*Note: This post is part 3 of a 3-part series: part 1, part 2, part 3.* ** Read more...**

## Linear and Logistic Regression, Part 2: Fitting the Models

*Note: This post is part 2 of a 3-part series: part 1, part 2, part 3.* ** Read more...**

## Linear and Logistic Regression, Part 1: Understanding the Models

*Note: This post is part 1 of a 3-part series: part 1, part 2, part 3.* ** Read more...**