YorkJ

Python

All Algorithms implemented in Python

interviews

Everything you need to know to get the job.

architect-awesome

后端架构师技术图谱

ray

Ray is an AI compute engine. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.

mmdetection

OpenMMLab Detection Toolbox and Benchmark

viper

Go configuration with fangs

docker_practice

Learn and understand Docker&Container technologies, with real DevOps practice!

mlflow

Open source platform for the machine learning lifecycle

telegraf

Agent for collecting, processing, aggregating, and writing metrics, logs, and other arbitrary data.

dask

Parallel computing with task scheduling

awesome-semantic-segmentation

:metal: awesome-semantic-segmentation

modin

Modin: Scale your Pandas workflows by changing a single line of code

libzmq

ZeroMQ core engine in C++, implements ZMTP/3.1

imgproxy

Fast and secure standalone server for resizing and converting remote images

Halide

a language for fast, portable data-parallel computation

SparkInternals

Notes talking about the design and implementation of Apache Spark

go.uuid

UUID package for Go

shuttle

A simple shortcut menu for macOS

DIGITS

Deep Learning GPU Training System

glow

Code for reproducing results in "Glow: Generative Flow with Invertible 1x1 Convolutions"

weld

High-performance runtime for data analytics applications

bimg

Go package for fast high-level image processing powered by libvips C library

spark-ml-source-analysis

spark ml 算法原理剖析以及具体的源码实现分析

how-to-optimize-gemm

h2o-tutorials

Tutorials and training material for the H2O Machine Learning Platform

diracnets

Training Very Deep Neural Networks Without Skip-Connections

blislab

BLISlab: A Sandbox for Optimizing GEMM

tcpshm

A connection-oriented persistent message queue framework based on TCP or SHM(shared memory)

Tensorflow-MultiGPU-VAE-GAN

A single jupyter notebook multi gpu VAE-GAN example with latent space algebra and receptive field visualizations.

Shufflenet-v2-Pytorch

Perfect implement. Model shared. x0.5 (Top1:60.646) and 1.0x (Top1:69.402).