Peng Liu's repositories
Super-Resolution
图像超分辨率算法的实现
5622_PacMan_NN
Implementation of a neural network with a genetic algorithm to solve Ms. PacMan.
arxiv-sanity-preserver
Web interface for browsing, search and filtering recent arxiv submissions
awesome-deep-vision
A curated list of deep learning resources for computer vision
awesome-rnn
Recurrent Neural Network - A curated list of resources dedicated to RNN
caffe-deconvnet
A deconvolutional network in caffe
caffe-vdsr
A Caffe-based implementation of very deep convolution network for image super-resolution
conv_arithmetic
A technical report on convolution arithmetic in the context of deep learning
deep-residual-networks
Deep Residual Learning for Image Recognition
DeepEvolve
Rapid hyperparameter discovery for neural nets using genetic algorithms
deeplearningbook-chinese
Deep Learning Book Chinese Translation
DeepRecommender
Deep learning for recommender systems
Detectron
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
devol
Automated deep neural network design via genetic programming
Genetic-CNN
CNN architecture exploration using Genetic Algorithm
Machine-Learning-Tutorials
This repository contains a collection of tutorials that are related to different machine learning concepts, tools and algorithms
MCWNNM_ICCV2017
Matlab Code
models
Models built with TensorFlow
neural-network-genetic-algorithm
Evolving a neural network with a genetic algorithm.
papers
Summaries of papers on deep learning
pose-hg-train
Training and experimentation code used for "Stacked Hourglass Networks for Human Pose Estimation"
PRMLT
Matlab code for machine learning algorithms in book PRML
reproducible-image-denoising-state-of-the-art
Collection of popular and reproducible image denoising works.
Single-Image-Super-Resolution
A collection of high-impact and state-of-the-art SR methods
soft-eng-interview-prep
Everything you need to know for a Software Engineering interview
Super-Resolution.Benckmark
Benchmark and resources for single super-resolution algorithms