Thursday, April 19, 2018

10 simple rules for making research software more robust

Scientific software is often developed by a single person, usually a graduate student or a postdoc. The code might run just fine on their own computer, but what if someone else wants to run it? More often than not, scientific code is poorly documented, might work in unexpected ways (or not at all), rely on nonexistent paths or resources, or might simply fail to reproduce what was published in the paper. To avoid many common challenges associated with scientific code, Morgan Taschuk from the Ontario Institute for Cancer Research (Toronto, Ontario, Canada) and Greg Wilson from the Software Carpentry Foundation (Austin, TX) have come up with a list of ten simple rules.

Tuesday, April 3, 2018

Machine Learning for OpenCV in Korean and Japanese

Good news: I am happy to announce that Machine Learning for OpenCV is being translated into Japanese and Korean. The titles will appear later this year. Until then, consider grabbing an English copy—and make sure you download the latest code from GitHub.

Stay tuned for more!

Michael Beyeler
Machine Learning for OpenCV
14 July 2017
Packt Publishing Ltd., London, England
Paperback: 382 pages
ISBN 978-178398028-4

Sunday, December 31, 2017

Top 10 new GitHub features of 2017

As 2017 comes to a close, let's have a look at the best features that the GitHub developer team has introduced this year, ranging from protected branches to improved project management and completely new IDEs.

Friday, December 22, 2017

Holiday Sale: eBooks and videos for $5

Looking for a last-minute Christmas gift? Packt Publishing is holding a special holiday sale: Act now to get the eBook version of Machine Learning for OpenCV, OpenCV with Python Blueprints, and OpenCV: Computer Vision Projects with Python for only $5 each!

Limited time only.

Saturday, December 2, 2017

How to classify iris species using logistic regression

Despite its name, logistic regression can actually be used as a model for classification. In this post I will show you how to build a classification system in scikit-learn, and apply logistic regression to classify flower species from the famous Iris dataset.