Convolutional Neural Net to Classify Traffic Signs

This repository contains implementation of some deep neural networks by Keras.

Python jupyter notebook project on learning and detecting traffic signs

LeNet5 on MNIST with SGD and Adam

Implementing the Naive Bayes to perform classification over MNIST and CIFAR-10 datasets. Results are compared with Bayesian Linear regression and LeNet5 architecture

南开大学 机器学习（谢晋老师） EX2（大作业）2023年秋 LeNet5实现

Training and testing various CNN models for image classification.

Basic baseline training script of MNIST

A simple and extensible image recognition neural network framework

Enhanced LeNet-5 for MNIST digit classification with minor modifications like dropout for better generalization and OneCycleLR training. High-accuracy baseline for handwritten digit recognition and CNN experiments.

Implemetation of CNN models training with MNIST, CIFAR10 datasets

CNN architectures in C++

Implementation and comparison of SVM and LeNet5 on MNIST

Simple LeNet5 for MNIST dataset with PyTorch and achieves 99.13% accuracy on the test data.

基于Pytorch复现的Yann LeCun大佬于1998年提出的LeNet5模型。

lenet5的tensorflow实现

Numpy implementation of deep learning

Convolutional Neural Network implemenation from scratch in python numpy

Summary & Implementation of Deep Learning research paper in Tensorflow/Pytorch.

Pytorch implementation(LeNet, VGGNet, GAN, UNet, Mask R-CNN, ...)