Essential math for Data Science: Introduction to matrices and the matrix product

As you saw in Essential Math for Data Science, vectors are a useful way to store and manipulate data. You can represent them geometrically as arrows, or as arrays of numbers (the coordinates of their ending points). However, it can be helpful to create more complicated data structures – and that is where matrices need to be introduced.

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Stochastic Processes, a Beginner’s Guide

Tropical Fish

Many of you might not have heard of stochastic processes before and be wondering how they might be relevant to you. Firstly, statisticians might find stochastic processes a nice way of modeling probabilistic events. Additionally, those interesting in reinforcement learning may find that this information solidifies their understanding of RL concepts such as Markov Chains. Lastly, this article is short and easy-to-follow, so if you’re curious about stochastic processes themselves, then this is a good introduction.

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A Quick Introduction to Time Series Analysis

In my first article on Time Series, I hope to introduce the basic ideas and definitions required to understand basic Time Series analysis. We will start with the essential and key mathematical definitions, which are required to implement more advanced models. The information will be introduced in a similar manner as it was in a McGill graduate course on the subject, and following the style of the textbook by Brockwell and Davis.

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AI and Machine Learning are our best bet to keep saving the World from Climate Catastrophe

Many equations apply to Nuclear Fusion including the Maximum Entropy Principle. Fusion increases entropy. Think of unsolved equations relating to Nuclear Fusion as hardness problems. Whoever solves these problems or contributed towards software that solves these problems, helped achieve one of the biggest tasks in modern engineering and physics this century.

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