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Deep Learning Package in Python Based on The Deep Learning Tutorials and Theano
Deep learning recommend system with TensorFlow
Giant Language Model Test Room
PyTorch implementation of "Learning to Discover Cross-Domain Relations with Generative Adversarial Networks"
Example code for the Siggraph Asia Tutorial CreativeAI
DML4Rec
Convolutional and recurrent deep neural networks for text sentiment analysis.
Text Classification from GloVe Embeddings
Doc2Vec implementation in tensorflow.
Launch Etsy's Skyline (part of Kale) using Docker
A set of metrics for feature selection from text data
Build a model that automatically classifies the products based on their images for Cdiscount.com
EANN: event-adversarial neural networks for multi-modal fake news detection
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.
Implementation of seq2seq model for English to Mongolian translation
A fine-tuning BERT model for detect and to binary classify fake news articles.
Fake news detection for DataCup competition
Fake news detection
Classify news stories as fake or truthful to help a social media platform reduce its risk of negative PR and related financial losses using Natural Language Processing (NLP) and Machine Learning
A fake-news detector model based on BERT trained and evaluated on Liar-Plus dataset.
Machine Learning to detect fake news articles (FakeNewsNet, Google BERT)
Used BERT embeddings on LIAR dataset for multi-class classification to achieve state-of-the-art results.
Fake news detector based on the content and users associated with it using BERT and Graph Attention Networks (GAT).
Fake News Detection Using Recurrent Neural Networks (RNNs) & Long Short Term Memory (LSTM).
A web app which interacts with a deployed SageMaker model performing detection on a fake news dataset.
Fake NEWS detector using LIAR dataset.
Fake News Detection with Machine Learning
Implementation of BERT baseline for fakenews detection (fnc-1)
This is a dataset for fake news detection research
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.