Zheng Li's repositories
Stock-Market-System
This project aimed at obtaining the target stock data from websites and downloading to present in different methods. It is a task for a Java Module, so not for personal users.
halo
✍ Halo 一款现代化的个人独立博客系统
stanford-cpp-library
Stanford C++ library used in CS106B/X courses
opencv
Open Source Computer Vision Library
fashion-mnist
A MNIST-like fashion product database. Benchmark :point_right:
RAS-Challenge-2019
The software respository for the RAS Challenge held at the AMRC in June 2019.
Detectron
FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.
Reinforcement-learning-with-tensorflow
Simple Reinforcement learning tutorials
rosbook
Example code to accompany the book Programming Robots with ROS
d2l-zh
《动手学深度学习》，英文版即伯克利深度学习（STAT 157，2019春）教材。面向中文读者、能运行、可讨论。
caffe
Caffe: a fast open framework for deep learning.
v3-utility-library
Utility libraries for Google Maps JavaScript API v3
ML-Tutorial-Experiment
Coding the Machine Learning Tutorial for Learning to Learn
neural-networks-and-deep-learning
Code samples for my book "Neural Networks and Deep Learning"
MobileNet-SSD
Caffe implementation of Google MobileNet SSD detection network, with pretrained weights on VOC0712 and mAP=0.727.
BookCNN
《深度卷积网络：原理与实践》现已在淘宝天猫京东当当发售. 这里是其中的代码下载.
assignment-5-LiZheng1997
assignment-5-LiZheng1997 created by GitHub Classroom
assignment-4-LiZheng1997
assignment-4-LiZheng1997 created by GitHub Classroom
assignment-3-LiZheng1997
assignment-3-LiZheng1997 created by GitHub Classroom
assignment-2-LiZheng1997
assignment-2-LiZheng1997 created by GitHub Classroom
assignment-1-LiZheng1997
assignment-1-LiZheng1997 created by GitHub Classroom
All_Demo
Demo
booksource
《第一行代码 第2版》全书源代码
SqueezeNet
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
Tensorflow-Tutorial
Some interesting TensorFlow tutorials for beginners.
stanford_self_driving_car_learning
Stanford Code From Cars That Entered DARPA Grand Challenges
MobileNet
MobileNet build with Tensorflow