- Deep Learning for Analytical Engine — This repository contains an implementation of a convolutional neural network as a program for Charles Babbage’s Analytical Engine, capable of recognizing handwritten digits to a high degree of accuracy (98.5% if provided with a sufficient amount of training data and left running sufficiently long).
- Supervisor Trees in Go — A well-structured Erlang program is broken into multiple independent pieces that communicate via messages, and when a piece crashes, the supervisor of that piece automatically restarts it. […] Even as I have been writing suture, I have on occasion been astonished to flip my screen over to the console of Go program I’ve written with suture, and been surprised to discover that it’s actually been merrily crashing away during my manual testing, but soldiering on so well I didn’t even know.
- How to Avoid Brittle Code — If it hurts, do it more often.
- Developing Quantum Annealer Driven Data Discovery (Joseph Dulny III, Michael Kim) — In this paper, we gain novel insights into the application of quantum annealing (QA) to machine learning (ML) through experiments in natural language processing (NLP), seizure prediction, and linear separability testing.
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Data science: Where are we going? - DJ Patil, the U.S. government's first Chief Data Scientist, looks at the future of data science at Strata + Hadoop World 2015 in San Jose.
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