Machine learning is changing the way we do things, and it’s becoming mainstream very quickly.
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While many factors have contributed to this increase in machine learning, one reason is that it’s becoming easier for developers to apply it, thanks to open source frameworks.
If you’re not familiar with this technology, and feel confused about some of the terms used, such as “framework” and “library,” here are the definitions:
Framework. A vague term, to be sure; even those who regularly use it can’t agree on its exact definition. However, in most cases, "framework" refers to a bunch of programs, libraries and languages you have built to use in application development. Think of a framework as a base for getting started.
Library. A collection of objects or methods that your application uses. It’s a file with re-usable code that can be shared by many applications, so you don’t have to write the same code repeatedly. Instead, you link to the library.
As one online user put it: “The key difference between a library and a framework is 'inversion of control.' When you call a method from a library, you are in control. But with a framework, the control is inverted: The framework calls you.”
Still confused? Check out this helpful YouTube video about the difference between a framework and a library.
If you’re diving into machine learning in a big way, you’re probably seeking resources to help guide you. There are many frameworks available, but here are some of our favorites to help you get started.
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