Based on our record, PyTorch seems to be a lot more popular than ML5.js. While we know about 110 links to PyTorch, we've tracked only 9 mentions of ML5.js. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
PyTorch - An open source machine learning framework. PyTorch Tutorials - Tutorials and documentation. - Source: dev.to / 4 days ago
In this guide, we provided a comprehensive, step-by-step explanation of how to implement a simple GPT (Generative Pre-trained Transformer) model using PyTorch. We walked through the process of creating a custom dataset, building the GPT model, training it, and generating text. This hands-on implementation demonstrates the fundamental concepts behind the GPT architecture and serves as a foundation for more complex... - Source: dev.to / 11 days ago
PyTorch is a powerful and flexible deep learning framework that offers a rich set of features for building and training neural networks. - Source: dev.to / 20 days ago
Oddly enough, sometimes, the best way to learn is by putting forth incorrect opinions or questions. Recently, while wrestling with AI project complexities, I pondered aloud whether all Docker images with AI models would inevitably be bulky due to PyTorch dependencies. To my surprise, this sparked many helpful responses, offering insights into optimizing image sizes. Being willing to be wrong opens up avenues for... - Source: dev.to / 13 days ago
TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more... - Source: dev.to / about 2 months 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 1 year 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 / almost 2 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 2 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 2 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 2 years ago
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Amazon Machine Learning - Machine learning made easy for developers of any skill level
Keras - Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Machine Learning Playground - Breathtaking visuals for learning ML techniques.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Lobe - Visual tool for building custom deep learning models