Table of contents
ML5.js
Friendly machine learning for the web.
As ML5.js is an open source project, you can find more
open source alternatives and stats
on LibHunt.
Ease of Use
ml5.js is designed with simplicity in mind, making machine learning accessible to artists, creative coders, and students, even those without a robust background in AI or machine learning.
Browser-based
Operates directly in the browser, eliminating the need for any additional setup or dependencies, which makes it highly compatible with web projects.
Pre-trained Models
Includes a variety of pre-trained models for quick implementation of complex machine learning tasks like image classification, pose detection, and text generation.
Community and Documentation
Strong community support and well-documented guides and examples help new users get started quickly and find solutions to common issues.
Integration with p5.js
Integrates seamlessly with p5.js, a popular JavaScript library for creative coding, facilitating the development of interactive and visually engaging applications.
ML5.js is considered a good tool for web developers who want to incorporate machine learning into their projects without needing extensive knowledge of the underlying algorithms. It's particularly noted for its simplicity and ease of use.
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Check the traffic stats of ML5.js on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
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Check the "Domain Authority" of ML5.js on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about ML5.js on Reddit. This can help you find out how popualr the product is and what people think about it.
Libraries: Surprise for collaborative filtering (Python), TensorFlow.js for in-browser ML, ml5.js for approachable ML in JS. - Source: dev.to / 5 months ago
Ml5.js: Built on top of TensorFlow.js, it provides a user-friendly interface for implementing machine learning in web applications.โ. - Source: dev.to / over 1 year ago
Important APIs - ml5 for in-browser detection, face-api that uses tensorflow-node to accelerate on-server detection. VueUse for a bunch of useful component tools like the QR Code generator. Yahoo's Gifshot for creating gif files in-browser etc. - Source: dev.to / over 3 years ago
See also: https://ml5js.org/ "The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.". - Source: Hacker News / about 4 years ago
I used ml5js.org , p5js.org and https://teachablemachine.withgoogle.com to train the Banana images. When you create a new image project on Teachable Machine, you can output the p5js and basically use it right out of the box - I customized js, css, and html from there. Source: over 4 years ago
Going forward: I'll be 100% into JavaScript. You can use JavaScript in so many fields nowadays. Websites React, Mobile Apps React Native, Machine Learning TensorFlow & ML5, Desktop Applications Electron, and of course the backend Node as well. It's kind of a no-brainer. Of course, they all have specific languages that are better, but for now, JavaScript is a bit of a catch-all. - Source: dev.to / over 4 years ago
From other comments, a lot of JavaScript developers who want to use TensorFlow had never heard of TensorFlow.js or ml5.js! Source: over 4 years ago
Yeah they made ml5.js for this reason: https://ml5js.org/ I do feel like Google could do better communicating all of their different tools though. Their ecosystem is large and pretty confusing - they've got so many projects going on at once that it always seems like everyone gets fed up with them before they take a second pass and make them more friendly to newcomers. Facebook seems to have taken a much more... - Source: Hacker News / over 4 years ago
Yep, try looking into ml5. ML can be done in the browser using transfer learning with pre-trained models. Source: over 4 years ago
As the number of positive COVID-19 cases rises everywhere, mask-wearing is coming back in vogue. Read on to learn how to build an app to detect whether or not someone is wearing a mask in a Twilio Video call with ml5.js. - Source: dev.to / about 5 years ago
Ml5 is an open source library and community that builds upon the TensorFlow library. Ml5 doesn't require any other dependencies besides TensorFlow, so it's surprisingly easy to get started with. You can fork this image-recognition app from their GitHub and have the power of machine learning at your fingertips. - Source: dev.to / over 5 years ago
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