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.
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.
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.
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