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2019-autumn-recruitment-experience

2019届秋招面经集合

ad-papers

Papers on Computational Advertising

adaboost

基于AdaBoost算法训练表征人脸的Haar特征

adaboost-1

Adaptive Boost Algorithm

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

algorithm

architecture.of.internet-product

互联网公司技术架构，微信/淘宝/微博/腾讯/阿里/美团点评/百度/Google/Facebook/Amazon/eBay的架构，欢迎PR补充

awesome-deep-learning-papers-for-search-recommendation-advertising

Awesome Deep Learning papers for industrial Search, Recommendation and Advertising. They focus on Embedding, Matching, Ranking (CTR and CVR prediction), Post Ranking, Multi-task Learning, Graph Neural Networks, Transfer Learning, Reinforcement Learning, Self-supervised Learning and so on.

b2cweb

仿淘宝的B2C商城项目 一、项目介绍：本项目使用SSH框架和MySQL数据库。实现Spring对Hibernate和Struts的整合。目前实现功能：（1）前台功能：用户注册、商品展示、购物车功能(商品进行添加、删除、修改、查看操作)，订单提交。（2）后台功能：商品增删改查和订单管理。（3）商品搜索（搜索内容进行分词，提取关键字，模糊查询）。（4）QQ在线咨询功能。本项目基本实现完整的B2C网站的功能。二、项目部署：本项目使用Eclipse_EE + Tomcat7.0+ MySql5.6的开发环境。1、安装 Eclipse_EE、Tomcat、MySql软件。2、MySql数据库中创建shop数据库。(1)CREATE DATABASE shop CHARACTER SET utf8 DEFAULT CHARACTER SET utf8 COLLATE utf8_general_ci DEFAULT COLLATE utf8_general_ci ; (2）本项目中sql/shop.sql,执行shop.sql，SQL语句创建各种表单，和默认管理员用户 ，默认普通用户。管理员用户名：admin,密码：123.普通用户的用户名：throne212,密码 123. 3、本网站源码导入Eclipse中，网站就可以运行。 ../Shop/manager/index.jsp为后台管理界面。三、任何问题 给我发邮件：[email protected]。

basic-single-module-ssm

为了方便以后的一些问题的讲解特地准备一个最精简的 SpringMVC+Spring+Mybatis 框架整合，方便以后的一些问题的演示

bbs

Internal API to access the database for Diego.

beego

beego is an open-source, high-performance web framework for the Go programming language.

build-web-application-with-golang

A golang ebook intro how to build a web with golang

chinesener

A neural network model for Chinese named entity recognition

ckfinder

clmtools

tools for building face-models for clmtrackr

clmtrackr

Javascript library for precise tracking of facial features via Constrained Local Models

convolutionalemotion

A deep convolutional neural network system for live emotion detection

deepctr

Easy-to-use,Modular and Extendible package of deep-learning based CTR models.

deeprec

推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction

dien

django-shop

A Django based shop system

do-neural-networks-learn-faus-iccvw-2015

Experiment files for the paper "Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?", available here: http://arxiv.org/abs/1510.02969

docker-redis-cluster

Dockerfile for Redis Cluster (redis 3.0+)

draw

eges

emoodji

:smile: :video_camera: CLMTrackr tool which tracks emotions with a webcam and a big blob

emotion-detection-in-videos

The aim of this work is to recognize the six emotions (happiness, sadness, disgust, surprise, fear and anger) based on human facial expressions extracted from videos. To achieve this, we are considering people of different ethnicity, age and gender where each one of them reacts very different when they express their emotions. We collected a data set of 149 videos that included short videos from both, females and males, expressing each of the the emotions described before. The data set was built by students and each of them recorded a video expressing all the emotions with no directions or instructions at all. Some videos included more body parts than others. In other cases, videos have objects in the background an even different light setups. We wanted this to be as general as possible with no restrictions at all, so it could be a very good indicator of our main goal. The code detect_faces.py just detects faces from the video and we saved this video in the dimension 240x320. Using this algorithm creates shaky videos. Thus we then stabilized all videos. This can be done via a code or online free stabilizers are also available. After which we used the stabilized videos and ran it through code emotion_classification_videos_faces.py. in the code we developed a method to extract features based on histogram of dense optical flows (HOF) and we used a support vector machine (SVM) classifier to tackle the recognition problem. For each video at each frame we extracted optical flows. Optical flows measure the motion relative to an observer between two frames at each point of them. Therefore, at each point in the image you will have two values that describes the vector representing the motion between the two frames: the magnitude and the angle. In our case, since videos have a resolution of 240x320, each frame will have a feature descriptor of dimensions 240x320x2. So, the final video descriptor will have a dimension of #framesx240x320x2. In order to make a video comparable to other inputs (because inputs of different length will not be comparable with each other), we need to somehow find a way to summarize the video into a single descriptor. We achieve this by calculating a histogram of the optical flows. This is, separate the extracted flows into categories and count the number of flows for each category. In more details, we split the scene into a grid of s by s bins (10 in this case) in order to record the location of each feature, and then categorized the direction of the flow as one of the 8 different motion directions considered in this problem. After this, we count for each direction the number of flows occurring in each direction bin. Finally, we end up with an s by s by 8 bins descriptor per each frame. Now, the summarizing step for each video could be the average of the histograms in each grid (average pooling method) or we could just pick the maximum value of the histograms by grid throughout all the frames on a video (max pooling For the classification process, we used support vector machine (SVM) with a non linear kernel classifier, discussed in class, to recognize the new facial expressions. We also considered a Naïve Bayes classifier, but it is widely known that svm outperforms the last method in the computer vision field. A confusion matrix can be made to plot results better.

emotionrecognitiononhologram

A system that can recognize human facial expression and analyze emotions (i.e: happy, sad, angry...) using CV and Machine Learning. After that, the human emotion data is streamed to a Unity character which is projected on a DIY pyramid hologram.

exercise

exercise for nndl