GopherData

Users and developers of Go data processing, analytics, and visualization tools.

GopherData brings together “gophers” (or Go programmers) who use and develop data analysis tools. The organization does this by providing online resources and in-person events. The goals are to provide data analysis enthusiasts, across various domains, with outlets to discuss how best to apply Go and tools built with Go to analytics, scientific research, database development, machine learning, visualization, etc.

We aim to be community-driven and accessible to novice Go programmers, experienced Gophers, data scientists without Go experience, data engineers, and anyone else who is interested in Go for data analysis. On this site you will find related blog posts, a listing of events, and links to a variety of online forums for discussion and learning.

Topics

Data Science
Data Engineering
Scientific Computing
Numerical Computing
Machine Learning
Artificial Intelligence
Data Visualization
Data Cleaning/Munging
Databases

Activities

Online and in-person events

Conferences, meetups, talks
Community Driven Posts

Blogs, videos, and news
Curated Resources

Relevant packages, tooling, links, and more

Recent & Upcoming Events

GopherData Conference
Wed, Jan 1, 2042, 1st Annual GopherData Conference

Recent Posts

Building an ML-Powered Game AI using TensorFlow in Go

Thu, Aug 10, 2017

Based on a lightning talk given at GopherCon 2017 “Building an ML-Powered Game AI using TensorFlow in Go” Video / Slides (Author: Pete Garcin, Developer Advocate @ ActiveState, @rawktron on Twitter and @peteg on Gophers Slack) For GopherCon, we wanted to demonstrate some of the capabilities of the emerging machine learning and data science ecosystem in Go. Originally built as a demo for PyCon, I had put together a simple arcade space shooter game that features enemies powered by machine learning.

Building a distributed Trump finder

Wed, Apr 26, 2017 academic, hugo

A step-by-step guide to building a distributed facial recognition system with Pachyderm and Machine Box.

Deep Learning from Scratch in Go - Part 1: Equations Are Graphs

Wed, Apr 19, 2017

(Author: Chewxy, @chewxy on Twitter and Gophers Slack) Welcome to the first part of many about writing deep learning algorithms in Go. The goal of this series is to go from having no knowledge at all to implementing some of the latest developments in this area. Deep learning is not new. In fact the idea of deep learning was spawned in the early 1980s. What’s changed since then is our computers - they have gotten much much more powerful.