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afnetworking

A delightful networking framework for iOS, macOS, watchOS, and tvOS.

biojava

:book::microscope::coffee: BioJava is an open-source project dedicated to providing a Java framework for processing biological data.

caffe-cvprw15

:heart::coffee: Deep Learning of Binary Hash Codes for Fast Image Retrieval (CVPRW15)

cubic-spline-interpolation

Cubic Spline Interpolation provides numeric computing formula to interpolate curve. This source code was designed to draw a 3D curve. If you have any question or optimized idea, welcome to contact me.

customlrcview-master

自定义显示歌词的控件，实现了歌词的平滑向上滚动，当高亮歌词宽度超过了view的宽的时候，水平滚动

deep-learning-with-python

Example projects I completed to understand Deep Learning techniques with Tensorflow. Please note that I do no longer maintain this repository.

deep-photo-styletransfer

Code and data for paper "Deep Photo Style Transfer": https://arxiv.org/abs/1703.07511

elasticsearch

Open Source, Distributed, RESTful Search Engine

elasticsearch-head

A web front end for an elastic search cluster

fedavg

Implement FedAvg algorithm based on Tensorflow

fedavg_mnist_working_example

federated-averaging-tutorials

A set of tutorials to implement the Federated Averaging algorithm on TensorFlow.

gitblackbroad

gromacs

Public/backup repository of the GROMACS molecular simulation toolkit. Please do not mine the metadata blindly; we use https://gitlab.com/gromacs/gromacs for code review and issue tracking.

install-opencv

shell scripts to install different version of OpenCV in different distributions of Linux

kibana

:bar_chart: Kibana analytics and search dashboard for Elasticsearch

math7502-markov_chain_wiki

Markov chain wiki

mathmatics_course_scripts

minst-classifier

Scitkit-Learns KNeighborsClassifier trained on the MINST dataset

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!

muduo

A C++ non-blocking network library for multi-threaded server in Linux

openclinica

OpenClinica is the world's first commercial open source clinical trial software for Electronic Data Capture (EDC) Clinical Data Management (CDM).

pagerankalgorithm

pubchemtool

spring-boot-helloworld

weapp-demo

微信小程序（应用号）示例教程（豆瓣电影）, awesome wechat weixin weapp demo, wxapp demo

yii2-cropper

Yii2 Image Cropper InputWidget

you-get

:arrow_double_down: Dumb downloader that scrapes the web