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ai_player

通过手势识别控制播放器

ailearn

A lightweight package for artificial intelligence

ailearning

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

algonotes

公众号【浅梦的学习笔记】文章汇总:包含 排序&CXR预估，召回匹配，用户画像&特征工程，推荐搜索综合 计算广告，大数据，图算法，NLP&CV，求职面试 等内容

angular.js

HTML enhanced for web apps

bgp-ranking

For an Internet Service Provider, AS numbers are a logical representation of the other ISP peering or communicating with his autonomous system. ISP customers are using the capacity of the Internet Service Provider to reach Internet services over other AS. Some of those communications can be malicious (e.g. due to malware activities on an end-user equipments) and hosted at specific AS location. In order to provide an improved security view on those AS numbers, a trust ranking scheme will be implemented based on existing dataset of compromised systems, malware C&C IP and existing datasets of the ISPs.

bgpchart

Get charts on BGP prefixes and peers for a given AS-number.

bgperf

bgpmon

BGPmon

books

整理一些书籍 ,包含 C&C++ 、git 、Java、Keras 、Linux 、NLP 、Python 、Scala 、TensorFlow 、大数据 、推荐系统、数据库、数据挖掘 、机器学习 、深度学习 、算法等。

bosssensor

Hide screen when boss is approaching.

bullshitgenerator

Needs to generate some texts to test if my GUI rendering codes good or not. so I made this.

c

This is my C/C++ examples

caffe

Caffe: a Fast framework for neural networks. For the most recent version, check out branch dev. For a more stable version, check out branch master.

cnn-text-classification-tf

Convolutional Neural Network for Text Classification in Tensorflow

dataframe2ffm

DataFrame数据格式转换成FFM格式

deep-learning

deepffm

deeppatient

A Keras implementation of DeepPatient

deepstyle

django-mnist

Front-end to Tensorflow's MNIST classification tutorial

djangoserver

DjangoServer

doctorai

Repository for Doctor AI project

douban-recommender

基于Spark ML实现的豆瓣电影推荐系统

easyfalsk

easyrec

A framework for large scale recommendation algorithms.

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.

exabgp

The BGP swiss army knife of networking

fastfm

fastFM: A Library for Factorization Machines

fastnetmon

This is old freezed version! Please use https://github.com/pavel-odintsov/fastnetmon for actual but unstable code