Implementation of CNN on ZYNQ FPGA to classify handwritten numbers using MNIST database

This project shows how to use CNN to perform Chinese character recognition, a much more complicated task compared to MNIST digit recognition.

MNIST classification using Convolutional NeuralNetwork. Various techniques such as data augmentation, dropout, batchnormalization, etc are implemented.

CNN accelerated by cuda. Test on mnist and finilly get 99.76%

This CNN-based model for recognition of hand written digits attains a validation accuracy of 99.2% after training for 12 epochs. Its trained on the MNIST dataset on Kaggle.

Implementation of CNN to recognize hand written digits (MNIST) running for 10 epochs. Accuracy: 98.99%

基于Java实现CNN，并附MNIST和语音（MFCC特征）性别识别示例。

mnist classification with tensorflow ( nn, cnn, lstm, nlstm, bi-lstm, cnn-rnn)

从入门到精通！学习tensorflow的过程，记录一些模型:mnist_ml LeNet cifar10_cnn

【入门项目】这个仓库是用hls来实现手写数字识别CNN硬件(xilinx fpga)加速的代码

使用CNN实现对手写数字的识别(Pytorch)

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基于Pytorch的卷积神经网络MNIST手写数字识别 适用于Pytorch与神经网络入门学习

How to train a CNN to 99% accuracy on MNIST in less than a second on a laptop

t-sne visualization of mnist images when feature is represented by raw pixels and cnn learned feature

Projects include the application of transfer learning to build a convolutional neural network (CNN) that identifies the artist of a painting, the building of predictive models for Bitcoin price data using Long Short-Term Memory recurrent neural networks (LSTMs) and a tutorial explaining how to build two types of neural network using as input the MNIST dataset, namely, a CNN using Keras and a fully-connected network using TensorFlow.

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Using various CNN techniques on the MNIST dataset

A series of Jupyter Notebooks which build a simple CNN model which is trained using the Fashion MNIST dataset

Evaluation of fashion-MNIST with a simple cnn

CNN digit recognizer implemented in Keras Notebook, Kaggle/MNIST (0.995).

Handwritten digit recognition.

Implementation of the Center Loss Algorithm with CNN on MNIST dataset

opencv+CNN实现实时手写数字识别

基于MNIST手写数据训练的CNN手写数字识别

Image classification with PyTorch using Convolutional Neural Networks (CNNs). Trains on MNIST with convolution, pooling, and fully connected layers. Achieves over 99% accuracy with early stopping and checkpoints. Includes training/evaluation scripts, metrics, confusion matrix, training curves, and sample prediction visualizations.

This is a matlab implementation of CNN on MNIST

A sample project implementing TensorFlowSharp and a trained CNN network to classify MNIST images in the Unity Video Game Engine

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Conditional variational autoencoder applied to EMNIST + an interactive demo to explore the latent space.