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ad-papers

Papers on Computational Advertising

ai_medical_top_papers

ai_tutorial

精选机器学习，NLP，图像识别， 深度学习等人工智能领域学习资料，搜索，推荐，广告系统架构及算法技术资料整理。算法大牛笔记汇总

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/算法/机器学习(Machine Learning)/深度学习(Deep Learning)/自然语言处理(NLP)/C/C++/Python/面试笔记

alpha101-new

基于华泰研报对原alpha101代码进行简化和拓展

apachecn-dl-zh

ApacheCN 深度学习译文集

awesome-deep-reinforcement-learning-in-finance

🔬 A collection for those AI (RL / DL / SL / Evoluation / Genetic Algorithm) used in financial market. otherwise, we add Technology Analysis / Alpha Research / Arbitrage and other useful strategies tools & docs in quantitative finance market.

awesome-java-leetcode

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

awesome-quant

A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)

awesome-spider

爬虫集合

aws_usage

backtrader

bert-as-service

Mapping a variable-length sentence to a fixed-length vector using BERT model

bert-bilstm-crf-ner

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning

bert-use-demo

the demo for bert

book-1

building-machine-learning-projects-with-tensorflow

Building Machine Learning Projects with TensorFlow by Packt

chanvis

基于TradingView本地SDK的可视化前后端代码，适用于缠论量化研究，和其他的基于几何交易的量化研究。 缠论量化 摩尔缠论 缠论可视化 TradingView TV-SDK

charcnn_keras

The implementation of text classification using character level convoultion neural networks using Keras

chinese-poetry

最全中华古诗词数据库, 唐宋两朝近一万四千古诗人, 接近5.5万首唐诗加26万宋诗. 两宋时期1564位词人，21050首词。

chinese-word-vectors

100+ Chinese Word Vectors 上百种预训练中文词向量

cnn_lstm_for_text_classify

CNN，LSTM中文文本分类

coding

leetcode

coding-interview-university

A complete computer science study plan to become a software engineer — from John Washam

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

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

data-analysis

基于自构造函数的特征提取评分项目（缺失值处理，单变量相关性分析，特征评分，降维）

data-modeling

用Python实现常见机器学习方法的基本原理

data-science-interview-resources

A repository listing out the potential sources which will help you in preparing for a Data Science/Machine Learning interview. New resources added frequently.