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janhuenermann/neurojs

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janhuenermann/neurojs has 4,365 GitHub stars and 360 forks. GitHot measured -1 stars in 24 hours, -1 in 7 days, and -2 in 15 days. Last checked 2026-11-11 04:18 UTC. Last push 2023-10-10.

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Una biblioteca de JavaScript para aprendizaje profundo y aprendizaje por refuerzo.

4,365 estrellas360 forks

What you can do

  • Implements a full-stack neural-network based machine learning framework
  • Uniform and prioritised replay buffers
  • Advantage-learning (increasing the action-gap) https://arxiv.org/pdf/1512.04860v1.pdf
  • Support for deep-q-networks and actor-critic models (via deep-deterministic-policy-gradients)

Crecimiento de estrellas medido

in 24h
-1
in 7 days
-1
in 15 days
-2
in 1 month
Actualización retrasada

Cambios netos en períodos que se solapan, no totales diarios. Los datos no disponibles se dejan en blanco. Las estrellas indican interés, no calidad.

README.md

Original
neurojs

neurojs

neurojs is a JavaScript framework for deep learning in the browser. It mainly focuses on reinforcement learning, but can be used for any neural network based task. It contains neat demos to visualise these capabilities, for instance a 2D self-driving car.

Feel free to contribute. The development on such projects is more awesome in a community!

Note: this repo is no longer maintained since more general frameworks such as TensorFlow-JS emerged, which I recommend you to use.

Features

  • Implements a full-stack neural-network based machine learning framework
  • Extended reinforcement-learning support
    • Uniform and prioritised replay buffers
    • Advantage-learning (increasing the action-gap) https://arxiv.org/pdf/1512.04860v1.pdf
    • Support for deep-q-networks and actor-critic models (via deep-deterministic-policy-gradients)
  • Binary import and export of network configurations (weights etc.)
  • High-performance

Examples

Running the examples

npm install
npm start

Open http://localhost:8080/examples/ in your browser and select the demo you want to run.

What's next?

  • More examples (pong, pendulum, snake,improved cars, etc.)
  • Support for web workers
  • LSTM and backpropagation through time

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