It is an exciting time for Go as a data science language and for the #gopherdata movement. The ecosystem of tools is constantly improving, with both general purpose tools (e.g., for data frames and statistical analyses) and more specialized ones (e.g., for neural networks and graph-based algorithms) popping up every day. Recent weeks have been particularly exciting for those involved in the bioinformatics field. In addition to generic libraries for bioinformatics such as bíogo, which was recently reviewed in quite some detail in a two blog posts (part I and part II), the ecosystem of scientific workflow tools focusing on or being used in bioinformatics is also growing: Last week, another Go-based workflow orchestration tool, Reflow, was released as open source, by life science startup Grail Inc.
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.
A step-by-step guide to building a distributed facial recognition system with Pachyderm and Machine Box.
(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.
GopherData is here! Get involved and explore.