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When using gradient descent as learning algorithm for a regression problem, there are several ways to improve it. In the beginning, it is often unclear how to optimize it. This article shows it by choosing the right learning rate and initial parameters, applying feature scaling and vectorization.

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Machine learning often starts out with a linear regression and gradient descent in an univariate training set. But often your data correlation isn't linear. That's where polynomial regression comes into play and selecting a model type to fit your underlying data.

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A little refresher about matrix operations (addition, subtraction, multiplication and division) in linear algebra and about the different types of matrices: inverse and transpose matrices, and the identity matrix. The article applies those learnings in JavaScript. Furthermore, in the end of the article, there will be an example to demonstrate why matrices are beneficial for computations in machine learning.

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The article guides you through implementing linear regression with gradient descent in JavaScript. The article can be used as introduction to machine learning in JavaScript and as entry point to a whole blog post series.

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