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alldreamnoip

android-baiduvoice

Android 基于百度语音的语音交互

anlp19

Course repo for Applied Natural Language Processing (Spring 2019)

asgcn

Code and preprocessed dataset for EMNLP 2019 paper titled "Aspect-based Sentiment Classification with Aspect-specific Graph Convolutional Networks"

attention-is-all-you-need-pytorch

A PyTorch implementation of the Transformer model in "Attention is All You Need".

autogluon

AutoGluon: AutoML for Text, Image, and Tabular Data

br-agent

This is the repository of IJCAI'2022 paper: "'My nose is running.''Are you also coughing?': Building A Medical Diagnosis Agent with Interpretable Inquiry Logics".

caffe

Support LRCN(both rgb and optical-flow). This fork of BVLC/Caffe is dedicated to improving performance of this deep learning framework when running on CPU, in particular Intel® Xeon processors (HSW+) and Intel® Xeon Phi processors

caffe-speech-recognition

Speech Recognition with the Caffe deep learning framework, migrating to

covid19-nlp

Chinese Medical Dialogue Dataset for COVID19 Consultant

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

deepspeech

A TensorFlow implementation of Baidu's DeepSpeech architecture

dive-into-dl-pytorch

本项目将《动手学深度学习》（Dive into Deep Learning）原书中的MXNet代码实现改为PyTorch实现。

emo_net

Emotional Face Recognition using Deep Learning (Tensorflow)

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.

facial-expression-recognition

Facial expression recognition deep learning examples

facialexpressionrecognition

Deep learning for the facial expression recognition kaggle challenge

free-programming-books-zh_cn

:books: 免费的计算机编程类中文书籍，欢迎投稿

gt-deep-learning-for-sign-language-recognition

Learning Hand Features for Sign Language Recognition (GATech Course Project: Deep Learning for Perception CS8803-DL, Spring 2015)

iclr2021-workshop-challenge-track2-baseline

long-speech-recognition

长音频识别, 转换为中文

lwgkzl

Config files for my GitHub profile.

meddg

a large-scale high-quality medical dialogue dataset

ml-nlp

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

ml-useful-trick

mlpcp21.github.io

models

Models and examples built with TensorFlow

multiesc

This is the repository of EMNLP'2022 paper: "Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning".

named_entity_recognition

中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

news-search-engine

新闻搜索引擎