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Block Coordinate Descent Methods in Deep Learning
Code for the paper "Let’s Make Block Coordinate Descent Go Fast"
Sign language interpreters are currently required for interpreting the speech impaired people. This skill-based job of interpreters is cumbersome and hence the number of interpreters per capita across majority countries are very low or decreasing. We aim to harness technology in developing a powerful continuous sign language gestures recognition system. This computer vision-based approach will be used to recognise Argentinian sign language gestures from a video. Translating these sign language gestures is considered a monumental task in this field. The project proposes to investigate whether sign language gestures can be recognised by using a trained modified Inception V3 working as a feature selector and classifier, with a LTSM Recurrent Neural Network. Two separate approaches have been applied to recognise the Argentinian gestures. The Global Max Pooling approach outperforms the SoftMax approach, with a model accuracy of 86.10% on validation set and 75.2% on test set. Using the Inception V3 model as a feature extractor for LTSM RNN worked more efficiently and produced better results than using the Inception V3 model as a classifier. These results show the effectiveness of the research conducted. This research will help in classifying and recognising continuous sign language gestures based on machine vision. This in turn will assist people that are affected by speech and hearing impairment in understanding, translating and recognising sign gestures.
small cpp project
深度学习入门开源书,基于TensorFlow 2.0案例实战。Open source Deep Learning book, based on TensorFlow 2.0 framework.
implementation of paper (Accelerated Mini-batch Randomized Block Coordinate Descent Method) and linear regression method (Lasso)
This is implementation of Coordinate Descent for Lasso.
Fast and accurate human pose estimation in PyTorch. Contains implementation of "Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose" paper.
Machine Learning Algorithms implementations
Siamese Network implementation using Pytorch
A simple reinforcement learning simulation engine for OpenAI's gym.
Tensorflow implement of paper: Sequence to Sequence: Video to Text
Sign Language Transformers (CVPR'20)
Implementation of "Sequence to Sequence – Video to Text"
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.