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An introduction to deep learning with Brain.js - LogRocket Blog

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Using Brain.js is a fantastic way to build a neural network. It learns the patterns and relationship between the inputs and output in order to make a somewhat educated guess when dealing with related issues. One example of a neural network is Cloudinary's image recognition add-on system. I was also shocked the first time I read the documentation of Brain.js, In this post, we will discuss some aspects of understanding how neural networks work.


You can build a neural network in JavaScript even if you don't really understand neural networks Codementor

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I should really start by admitting that I'm no expert in neural networks or machine learning. To be perfectly honest, most of it still completely baffles me. But hopefully that's encouraging to any fellow non-experts who might be reading this, eager to get their feet wet in M.L. Machine learning was one of those things that would come up from time to time and I'd think to myself "yeah, that would be pretty cool… but I'm not sure that I want to spend the next few months learning linear algebra and calculus." Like a lot of developers, however, I'm pretty handy with JavaScript and would occasionally look for examples of machine learning implemented in JS, only to find heaps of articles and StackOverflow posts about how JS is a terrible language for M.L., which, admittedly, it is. Then I'd get distracted and move on, figuring that they were right and I should just get back to validating form inputs and waiting for CSS grid to take off.


Understanding How Machines Learn, Through Prototyping – Big Tomorrow

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This is the second article in a larger series exploring the intersection of design and existing artificial intelligence technology through experiments, prototypes and concepts. We believe this is a critically important topic for the design community and beyond, so we're sharing what we learn along the way. Let's start by getting something out of the way: we're not machine learning experts -- we don't publish research about new algorithmic breakthroughs and we're not especially good at math. But we're curious about what to do with all the machine learning capability already floating around out in the world, and we're bullish about how far a'good enough' understanding can often take you. So how might non-experts begin to play with machine learning?


Using Machine Learning to Improve UI/UX

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The world of UI/UX is changing every month. What if you could use machine learning to help you keep up with all of the changes? Machine learning can help developers make more user-friendly web applications. Learn some background on machine learning and algorithms and see examples of where Brain.js The world of UI/UX is changing every month.


8 Machine Learning JavaScript Frameworks to Explore - DZone AI

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JavaScript developers tend to look out for JavaScript frameworks that can be used to train machine learning models based on different machine learning algorithms. In this post, you will learn about different JavaScript framework for machine learning. Deeplearn.js is an open-source machine learning JavaScript library by Google, which can be used for different purposes such as training neural networks in the browser, understanding ML models, for education purposes, etc. You can run pre-trained models in inference mode. One can write the code in Typescript (ES6 JavaScript) or ES5 JavaScript.