Pinned Repositories
-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 🦠
itsmysyntax's Repositories
itsmysyntax/beautiful-curves
A simple generative drawing app using the html5 canvas
itsmysyntax/canvas
use javascript create very beautiful canvas demo
itsmysyntax/cd4ml-workshop
Repository with sample code and instructions for "Continuous Intelligence" and "Continuous Delivery for Machine Learning: CD4ML" workshops
itsmysyntax/covid_tracker
A tracking app for tracking covid-19 cases around the world
itsmysyntax/dockerfiles
Various dockerfiles including chrome-headless, lighthouse and other tooling.
itsmysyntax/ffmpeg-examples
Some examples base on ffmpeg apis
itsmysyntax/ffmpeg-gpu
FFmpeg 4.0 with NVIDIA P4 GPU Driver Support
itsmysyntax/ffmpeg-nvenc
Dockerfile to execute ffmpeg including HW acceleration by GPU(nvenc)
itsmysyntax/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!
itsmysyntax/PConv-Keras-for-google-colab
Keras implementation of "Image Inpainting for Irregular Holes Using Partial Convolutions"
itsmysyntax/poster
A beautiful canvas poster
itsmysyntax/react-bouncing-balls
React component to make google doodle style bouncing balls from an arbitrary image
itsmysyntax/react-flask-ml-template
Project template for react, flask, machine learning
itsmysyntax/romania-elections
A map showing 2019 European Parliament election results in Romania
itsmysyntax/youtube-vlc
1080p or 4K YouTube playback in VLC using youtube-dl, without downloading first.