There is a screencast explaining how to train a neural network with Brain.js.
Since training takes up a lot of resources, it is preferred to run the library in a Node.js environment, although a CDN browser version can also be loaded directly onto a web page. There is a tiny demo on their website that can be trained to recognize color contrast.
In this library you can find the necessary algorithms for the creation of architecture-independent neural networks. Thanks to this, it can be used for the implementation of any type of neural network. However, different architectures already implemented are also included. These include multilayer perceptions, multilayer long and short term memory networks (LSTM), liquid state machines or Hopfield networks. This makes it possible to quickly test and evaluate different automatic learning algorithms. Like Brain, it is possible to use the library both in Node.js and in the browser.
The project also includes an introduction to neural networks along with a series of practical demonstrations and other tutorials
This is the web version of the popular library with which you can train neural networks in a browser. It can also be used for running pre-trained
This library allows you to quickly implement neural networks both in the browser and in Node.js. It includes different architectures such as perceptrons, LSTM, GRU, Nark and others.
Deep playground is a web application with which you can build and visualize neural networks. It has an elegant interface with which it is possible to control the input data, the number of neurons, the algorithm to be used and other parameters with which to affect the results.
The application code can be downloaded and studied from its public repository.
There is also a lot to learn from the app behind the scenes - the code is open-source and uses a custom machine learning library that is written in TypeScript and well documented.
DeepForge is a visual development environment for deep learning. It allows for the design of neural networks using a simple notebook type graphic interface, where users can get real-time feedback on executions and share them in real time. Models can be trained on remote machines and incorporates the possibility of using version control.
The project runs in the browser and is based on Node.js and MongoDB, making the installation process very familiar to most web devs.
This is a web application that can be used on the mobile phone to identify objects and name them in different languages. The application is based entirely on web technologies and uses two Google Machine Learning APIs: Cloud Vision for image recognition and the Translation API for natural language translations.
These other libraries are also very interesting:
13. Land Lines
Land Lines is an interesting Chrome Web experiment that finds satellite images of Earth, similar to doodles made by the user. The app makes no server calls: it works entirely in the browser and thanks to clever usage of machine learning and WebGL has great performance even on mobile devices. You can check out the source code on GitHub or read the full case study here.
He has been working on the Internet since 1994 (practically a mummy), specializing in Web technologies makes his customers happy by juggling large scale and high availability applications, php and js frameworks, web design, data exchange, security, e-commerce, database and server administration, ethical hacking. He happily lives with @salvietta150x40, in his (little) free time he tries to tame a little wild dwarf with a passion for stars.
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