wangjie1450's repositories
Composite-Quantization-for-Approximate-Nearest-Neighbor-Search
Implemented the Composite Quantization algorithm for Nearest Neighbor search and performed analysis on SIFT, GIST and MNIST datasets.
CNN_FPGA_ZYNQ_PYNQ
hls code zynq 7020 pynq z2 CNN
LeNet-on-Zynq
Simulating implement of LeNet network on Zynq-7020 FPGA
pytorch-cifar10-notebook
Training LeNet, VGG, ResNet, DenseNet on CIFAR10 with PyTorch.
matlab-mnist-two-layer-perceptron
A two layer perceptron implemented in MatLab to recognize handwritten digits based on the MNIST dataset.
brain
This repo contains Go implementation and corresponding APIs for acceleration of Binarized Neural Network (BNN) on FPGAs.
dbm
Simple implementation of Deep Boltzmann Machine
MNist_classification
Classification of the famous dataset of Mnist, applying the Support Vector Machine algorithm and a Radial Basis Function Network. These assignments were for the course Neural Networks from Aristotle University of Thessaloníki. February 2017.
Digit-Recognition-ANN
Training an artificial neural network using back-propagation on MNIST dataset
DeepLearnToolbox
Matlab/Octave toolbox for deep learning. Includes Deep Belief Nets, Stacked Autoencoders, Convolutional Neural Nets, Convolutional Autoencoders and vanilla Neural Nets. Each method has examples to get you started.
FusionGAN
FusionGAN: A generative adversarial network for infrared and visible image fusion
Elongate-BNN-demonstrator
Elongate technology demonstrator for zcu102 board using the binarized neural network
BNN
HLS code for a BNN accelerator
PNAS-Binarized-Neural-Networks
Progressive Neural Architecture Search coupled with Binarized CNNs to search for resource efficient and accurate architectures.
FPGA-Accelerator-for-AES-LeNet-VGG16
FPGA/AES/LeNet/VGG16
rbm_ising
Restricted Boltzmann Machine for the Ising model in PyTorch
TSP
求解TSP问题的几种算法
GA_tsp
TSP问题是指假设有一个旅行商人要拜访n个城市，他必须选择所要走的路径，路径的限制是每个城市只能拜访一次，而且最后要回到原来出发的城市。路径的选择目标是要求得的路径路程为所有路径之中的最小值。使用遗传算法解决att48问题，即48个城市的旅行商问题，该问题目前的最优解是10628，受个别参数影响，所设计的算法所得到的最优结果是10648，相对误差为0.18818216%。
GFN-dehazing
Gated Fusion Network for Single Image Dehazing
SVM-MNISTS
这是一个小的project,使用SVM来进行MNIST数据集的训练.内部的函数使用的是python自带的sklearn来做的。准确率达到了94%以上。
CO-EA
Conducted research on Dynamic Stochastic Demand Vehicle Routing Problem Based with Co-Evolutionary Algorithm using Matlab for capstone project Applied the Co-Evolutionary algorithm, heuristic local search strategy, adaptive similarity grouping, Hamming distance similarity metric and parallel and distributed population group co-evolutionary strategy
convolution_network_on_FPGA
CNN acceleration on virtex-7 FPGA with verilog HDL
ising
MATLAB library for simulating the Ising Model.
Picture_Evolution
Classify MNIST_Data, then use an evolutionary algorithm to generate an image resembling the classified number (in less than 30 seconds)
AutoEncoder-for-MNIST
AutoEncoder for MNIST data
Spiking-Restricted-Boltzmann-Machine
RBM implemented with spiking neurons in Python. Contrastive Divergence used to train the network.
ML-Algorithms-Implementation-MNIST
Implementations for SVM(Multi-classifier), Perceptron ,KNN
zynqnet
Master Thesis "ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network"
ising-model-1
Fit an Ising model with neural spike train data using Minimum Probability Flow Learning. Based on code from Jascha Sohl-Dickstein.
Python_Machine_Learning_Sklearn_MNIST
mnist数据集的基本数据分析方法，包括SVM算法。