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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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The article showcases how to setup an automated Slack invitation for a JavaScript and/or React application. Why would you want an automated Slack invitation in the first place? My personal motivation was it to create a like minded community of people learning React, but the idea about a Slack Channel started out on Twitter

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There a various tutorials showing you how to host your web applications with a couple of lines on any hosting solution. But that's it. You are able to host your application somewhere, but there are a lot of open questions. What about using an own domain? What about securing it with SSL? What about hosting multiple applications side by side yet keeping it cost efficient by not taking up to much ressources? This article should fill the void and provide you a solution on how to host multiple applications on Digital Ocean.

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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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The articles gives you a huge brain dump of mine about why I built my own course platform, what I used to accomplish it, what challenges I faced, and what decisions I had to make along the way. The article aims to give you a couple of valuable insights into launching your own platform, a web application or in general your own product

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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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The Road to learn React offers you a foundation before you start to dive into the more advanced React ecosystem. It has less tooling and less external state management, but more React. It explains general concepts and patterns.

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It is often a false assumption that MobX can only be used with decorators. That's not true. The article explains how to use MobX in a create-react-app or Next.js application with and without using decorators. There is also one alternative to avoid ejecting when using create-react-app.

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There is a high likelihood that there are already a multitude of articles about the new React.js 16 release. But for learning about the changes of the library myself, I wanted to read up all the new React 16 features and improvements too. Maybe it is useful for you if I share briefly what I have learned. In the next days, I hope to find the time to update my articles and course content accordingly to the changes.

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