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A pet web app built with Apollo-Grapql and React
Chrome Headless docker images built upon alpine official image
A simple generative drawing app using the html5 canvas
Getting Started with Caching (Redis + NodeJS)
use javascript create very beautiful canvas demo
Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshops
A simple mobile app developed with Flutter to visualize Covid19 statistics 🦠
A tracking app for tracking covid-19 cases around the world
A cross platform-app made with flutter of latest updates of Covid-19
A dockerized TypeScript-Express App boilerplate with MongoDB and Github Actions
Base image for Bayes dev projects using React on top of npm.
Various dockerfiles including chrome-headless, lighthouse and other tooling.
Facebook clone using React, Appollo and GraphQL.
Some examples base on ffmpeg apis
FFmpeg 4.0 with NVIDIA P4 GPU Driver Support
Dockerfile to execute ffmpeg including HW acceleration by GPU(nvenc)
🕹 3D video game experiments. three.js, TypeScript, React, Redux and GLSL shaders at once.
This is a fullstack development tutorial which uses Docker, TypeScript, React+Redux, and MicroService
A sample node.js application that uses github actions for CI and CD
Simple apps made with ArcGIS API for JavaScript
In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits! GANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exploded in popularity. Here are a few examples to check out: Pix2Pix CycleGAN & Pix2Pix in PyTorch, Jun-Yan Zhu A list of generative models The idea behind GANs is that you have two networks, a generator 𝐺 and a discriminator 𝐷 , competing against each other. The generator makes "fake" data to pass to the discriminator. The discriminator also sees real training data and predicts if the data it's received is real or fake. The generator is trained to fool the discriminator, it wants to output data that looks as close as possible to real, training data. The discriminator is a classifier that is trained to figure out which data is real and which is fake. What ends up happening is that the generator learns to make data that is indistinguishable from real data to the discriminator. The general structure of a GAN is shown in the diagram above, using MNIST images as data. The latent sample is a random vector that the generator uses to construct its fake images. This is often called a latent vector and that vector space is called latent space. As the generator trains, it figures out how to map latent vectors to recognizable images that can fool the discriminator. If you're interested in generating only new images, you can throw out the discriminator after training. In this notebook, I'll show you how to define and train these adversarial networks in PyTorch and generate new images!
Keras implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions"
A beautiful canvas poster
React component to make google doodle style bouncing balls from an arbitrary image
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.