A neural network Swift playground, with no third party dependencies.

A MATLAB implementation of the TensorFlow Neural Networks Playground seen on http://playground.tensorflow.org/

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A website in which you can customize, train and visualize changes in your neural networks as they learn. You can also upload your own datasets and change any parameters

Feed forward neural net examples to learn spiral problem like playground.tensorflow.org (tensorflow, pytorch, numpy)

Online neural network playground to see how neural network works in real-time!

A playground for feed forward neural networks

Collection of simple neural network examples.

NeuralNetworkPlayground is a Java based tool to train single hidden layer Neural Networks on your own datasets and visualize their decision boundary in a GUI interface.

Neural network tests, applications implemented in Javascript.

Solution to (some) Machine Learning Problems with Multi Layer Perceptron (MLP)

An accessible, interactive web application that enables beginners to learn neural networks by visually designing, training, and testing MLPs without writing code

A visual Deep Learning Framework for the Web - Built with WebGPU, Next.js and ReactFlow.

Neural Network Playground

Playground for experimenting with Neural Networks

The Neural Network Playground is an interactive, browser-based tool designed to help users visualize, experiment with neural network and understand the behavior of small neural networks.

A toy Neural Network-based linear image classifier based on OpenCV. It is designed to be trained on any grayscale dataset.

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This is a simple implementation of a NN. An XOR binary classification problem is used.

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Neural Networks can learn (almost!) anything.

just for fun.

This is a project designed to build a small neural network from scratch, test it, and study the influence of learning parameters.

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Interactive exploration of a basic neural net.

neural networks playground

No description available

A pure-Python tool to design, train, and visualize neural networks from scratch. Build your own network, tweak its architecture, and watch it learn on classic datasets like XOR or spirals.

Allows the user to construct their own neural network by deciding the number of hidden layers, number of neurons per hidden layer, activation and loss functions.