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-pet-app-with-apollo-graphql-with-react

A pet web app built with Apollo-Grapql and React

alpine-chrome

Chrome Headless docker images built upon alpine official image

beautiful-curves

A simple generative drawing app using the html5 canvas

blog-redis-nodejs

Getting Started with Caching (Redis + NodeJS)

canvas

use javascript create very beautiful canvas demo

cd4ml-workshop

Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshops

cml

covid19stats

A simple mobile app developed with Flutter to visualize Covid19 statistics 🦠

covid_tracker

A tracking app for tracking covid-19 cases around the world

covidupdates

A cross platform-app made with flutter of latest updates of Covid-19

docker-express-typescript-boilerplate

A dockerized TypeScript-Express App boilerplate with MongoDB and Github Actions

docker-node

docker-react

Base image for Bayes dev projects using React on top of npm.

dockerfiles

Various dockerfiles including chrome-headless, lighthouse and other tooling.

dummy

fb

Facebook clone using React, Appollo and GraphQL.

ffmpeg-examples

Some examples base on ffmpeg apis

ffmpeg-gpu

FFmpeg 4.0 with NVIDIA P4 GPU Driver Support

ffmpeg-nvenc

Dockerfile to execute ffmpeg including HW acceleration by GPU(nvenc)

firebasefunc

flight

🕹 3D video game experiments. three.js, TypeScript, React, Redux and GLSL shaders at once.

fullstack_development

This is a fullstack development tutorial which uses Docker, TypeScript, React+Redux, and MicroService

github-action-example-node

A sample node.js application that uses github actions for CI and CD

image-thumbnails

jsapi_demos

Simple apps made with ArcGIS API for JavaScript

mnist_gan

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!

nstream

pconv-keras-for-google-colab

Keras implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions"

poster

A beautiful canvas poster

react-bouncing-balls

React component to make google doodle style bouncing balls from an arbitrary image