lzdfeifei Goto Github PK

-optimization-test

vue项目优化问题

100-days-of-ml-code

100-Days-Of-ML-Code中文版

a-philosophy-of-software-design-zh

《软件设计的哲学》中文翻译 A Philosophy of Software Design

awesome-nlp-sentiment-analysis

:book: 收集NLP领域相关的数据集、论文、开源实现，尤其是情感分析、情绪原因识别、评价对象和评价词抽取方面。

awesome-public-datasets

A topic-centric list of high-quality open datasets in public domains. By everyone, for everyone!

awesome-thesis

A curated list of practical tips and tricks to help you achieve an awesome CS master thesis [WIP] - contributions are welcome

bilibili-vue

前端vue+后端koa，全栈式开发bilibili首页

cheatsheets-ai

Essential Cheat Sheets for deep learning and machine learning researchers

chinese-copywriting-guidelines

Chinese Copywriting Guidelines：中文文案排版指北（简体中文版）

chinese_dictionary

同义词表，反义词表，否定词表

common-code

常用代码汇总

d2l-practice

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

front-end-knowledge

前端知识系统梳理

gitdatav

基于Vue框架构建的github数据可视化平台

how-to-ask-questions-the-smart-way

本文原文由知名 Hacker Eric S. Raymond 所撰寫，教你如何正確的提出技術問題並獲得你滿意的答案。

lzdfeifei.github.io

mediacrawler

小红书笔记 | 评论爬虫、抖音视频 | 评论爬虫、快手视频 | 评论爬虫、B 站视频 ｜ 评论爬虫、微博帖子 ｜ 评论爬虫

my-javascript30

30 Day Vanilla JS Challenge

my-vitepress

nrlpapers

Must-read papers on network representation learning (NRL) / network embedding (NE)

paascloud-master

spring cloud + vue + oAuth2.0全家桶实战，前后端分离模拟商城，完整的购物流程、后端运营平台，可以实现快速搭建企业级微服务项目。支持微信登录等三方登录。

prml

PRML algorithms implemented in Python

pwc

Papers with code. Sorted by stars. Updated weekly.

python-cheatsheet

Comprehensive Python Cheatsheet

reflection_summary

算法理论基础知识应知应会

supermall

taiga_site

tips-of-feature-engineering

A feature engineering kit for each issue, to give you a deeper and deeper understanding of the work of feature engineering!