Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.

FPGA Accelerator for CNN using Vivado HLS

Artificial Intelligence Learning Notes.

Simple program to learn CNN (LeNet-5) in pure C

A hardware implementation of CNN, written by Verilog and synthesized on FPGA

The simplest implementation of LeNet5 with mnist in PyTorch. Accuracy: ~99%

pytorch and cifar10

Tutorials of deep learning for computer vision.

An Eyeriss Chip (researched by MIT, a CNN accelerator) simulator and New DNN framework "Hive"

Learning Deep Features for Discriminative Localization (2016)

a collection of my notes on deep learning

AutoDiff DAG constructor, built on numpy and Cython. A Neural Turing Machine and DeepQ agent run on it. Clean code for educational purpose.

Baseline classifiers on the polluted MNIST dataset, SJTU CS420 course project

Handwritten Digit Recognition Using Convolutional Neural Network by Python

FPGA and GPU acceleration of LeNet5

This code includes classification and detection tasks in Computer Vision, and semantic segmentation task will be added later.

A tiny implementation of LeNet (without deep learning framework)

Implementation of LeNet5 without any auto-differentiate tools or deep learning frameworks. Accuracy of 98.6% is achieved on MNIST dataset.

This repository presents an innovative approach to classifying blood groups using fingerprint images through deep learning techniques. The project explores state-of-the-art convolutional neural network (CNN) architectures, such as ResNet, VGG16, AlexNet, and LeNet, to analyze and predict blood groups accurately.

Classify traffic signs by three classic ConvNets architecture using GTSRB dataset.