Training a Neural network in Unity 3D to recognize handwritten digits from the MNIST dataset
-
Updated
May 12, 2019 - C#
Training a Neural network in Unity 3D to recognize handwritten digits from the MNIST dataset
Shows how to create a neural network from scratch in C# without a 3th party library
It reads handwritten numbers given an input of pixel values. A supervised learning, gradient descent, mini-batching, softmax-output-activation neural network that is meant to be trained on the MNIST dataset (dataset not included in this repository).
Digit recognition neural network using the MNIST dataset. Features include a full gui, convolution, pooling, momentum, nesterov momentum, RMSProp, batch normalization, and deep networks.
A from-scratch basic backpropagation neural network implemented in C#.
Quick test to use font data to create training/testing data and train the model with ML.Net.
Реализация алгоритма обратного распространения ошибки для обучения нейронной сети для распознавания рукописных цифр
MNIST - Handwritten Digit Classification Example
MNIST Neuronal Network drawing program
Add a description, image, and links to the mnist-handwriting-recognition topic page so that developers can more easily learn about it.
To associate your repository with the mnist-handwriting-recognition topic, visit your repo's landing page and select "manage topics."