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ai-recommendersystem

该仓库尝试整理推荐系统领域的一些经典算法模型

algo

数据结构和算法必知必会的50个代码实现

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

algorithms

剑指Offer（第二版）/程序员代码面试指南（第2版）/LeetCode/LintCode

awesome-multimodal-ml

Reading list for research topics in multimodal machine learning

awesome-recommender-systems

A curated list of awesome resources about Recommender Systems.

awesome-recsys

Must-read papers on recommendation systems (RecSys)

cvat

Computer Vision Annotation Tool (CVAT) is a web-based tool which helps to annotate video and images for Computer Vision algorithms

data-competition-topsolution

Data competition Top Solution 数据竞赛top解决方案开源整理

deep-learning-papers-reading-roadmap

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

deep-learning-with-python-notebooks

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

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

docs-l10n

Translations of TensorFlow documentation

dped

Software and pre-trained models for automatic photo quality enhancement using Deep Convolutional Networks

dr.sure

🏫DeepLearning学习笔记以及Tensorflow使用心得笔记。Dr. Sure会不定时往项目中添加他看到的最新的技术，欢迎批评指正。

dream

This is the public repository for our accepted CVPR 2018 paper "Pose-Robust Face Recognition via Deep Residual Equivariant Mapping"

eat_tensorflow2_in_30_days

Tensorflow2.0 🍎🍊 is delicious, just eat it! 😋😋

eesen

The official repository of the Eesen project

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.

face-recognition-and-liveness-detection

face-recognition-on-live-data

Figuring out the identity of a person from a video stream, using TensorFlow's Inception v3 Neural Retraining and OpenCV

face_detec_system

key:facenet,消息对列,人脸特征上传生成,kcf人脸追踪

facenet

Face recognition using Tensorflow

facerecognition-tensorflow

基于TensorFlow训练的人脸识别神经网络

facial-expression-recognition

Facial-Expression-Recognition in TensorFlow. Detecting faces in video and recognize the expression(emotion).

fun-rec

推荐系统中文教程

gan_liveness_detection

Attempt to Solve Face Liveness using GANs

happynet

Convolutional neural network that does real-time emotion recognition. HappyNet detects faces in video and images, classifies the emotion on each face, then replaces each face with the correct emoji for that emotion. Based on Caffe and the "Emotions in the Wild" network available on Caffe model zoo.

home_surveillance

Home surveillance system with facial recognition

hugectr_backend