I’m sure many of us are curious about the mathematics behind such algorithms — how does mathematics factor into these algorithms, and how can the manipulation of mathematical systems produce such stunning results on par with detecting COVID-19?Read More
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.Read More
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.Read More
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.Read More
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.Read More
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This is the first article in a series of articles where we will understand the “under the hood” workings of various ML algorithms, using their base math equations.
Siamese Net, Triplet Loss, and Circle Loss explained.Read More