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awesome-books

:books: 开发者推荐阅读的书籍

awesome-java-leetcode

:crown: LeetCode of algorithms with java solution(updating).

awesome-pronunciation

:speech_balloon: How to pronounce Programming words?

awsome-java

Great Java project on Github(Github 上非常棒的 Java 开源项目).

bigdataguide

大数据学习，从零开始学习大数据，包含大数据学习各阶段学习视频、面试资料

coding-interviews

剑指Offer

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计、Java、Python、C++

cs_python_notes

Some notes about computer sience and Python.

data-science-notes

数据科学的笔记以及资料搜集

deep-compression-pytorch

PyTorch implementation of 'Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding' by Song Han, Huizi Mao, William J. Dally

deep-learning-with-keras-notebooks

Jupyter notebooks for using & learning Keras

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

diy_blog

这是Django的一个作业，跟着过了一遍。

django-diy-blog

Basic blog site written in Django (part of MDN Django module assessment).

django-locallibrary-tutorial

Local Library website written in Django; example for the MDN server-side development Django module: https://developer.mozilla.org/en-US/docs/Learn/Server-side/Django.

django_local_library

febs-shiro

Spring Boot 2.2.1，Shiro1.4.2 & Layui 2.5.5 权限管理系统。预览地址：https://shiro.mrbird.cn:8080

git-test

gitskills

go-douban-spider

基于go实现的多线程豆瓣爬虫

java-concurrent-programming

:notebook: 《实战Java 高并发程序设计》笔记和源码整理

java-gold

:coffee: Java炼金小木屋

javaguide

「Java学习+面试指南」一份涵盖大部分Java程序员所需要掌握的核心知识。准备 Java 面试，首选 JavaGuide！

javalibrary

本藏馆主要珍藏Java体系核心技术书籍：涵盖Java核心基石（Java核心技术，并发编程，JVM）Java助辅要员（设计模式，linux内核，TCP/IP协议，Netty，Tomcat，Mysql） 上古单体架构 分布式朝代 微服务纪元 云原生前沿 架构演进 自动化运维

jvm_book

《深入理解Java虚拟机（第3版）》样例代码&勘误

knowledge-distillation-pytorch

A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

learn-java

recording the code while learning Java :blue_book:

leetcode

model-compression

model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net)；2、 pruning: normal、regular and group convolutional channel pruning；3、 group convolution structure；4、batch-normalization folding for quantization)

network-slimming

Network Slimming (Pytorch) (ICCV 2017)