Some virtual turkey for all the nerds out there: Enjoy all my Packt eBooks and videos for only $10 each! No promo code required, just head on over to the official Packt Publishing website and start shopping. Limited time only!
Happy Thanksgiving!
Now that Microsoft has acquired GitHub, many are looking to move their code to some other hosting platform. But, what are some good free alternatives to GitHub in 2018?
Part 2 of the video course of my latest book, Machine Learning for OpenCV, is now available!
Good news: Packt Publishing is turning my latest book, Machine Learning for OpenCV, into a video course!
Take advantage of Packt Publishing's Flash Sale: Head on over to packtpub.com to get the eBook version of Machine Learning for OpenCV, OpenCV with Python Blueprints, and OpenCV: Computer Vision Projects with Python for only $10 each!
Limited time only.
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.
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
Once a year, researchers meet at the University of Washington (UW) in Seattle as part of the Neural Computation and Engineering Connection to discuss what's new in neuroengineering and computational neuroscience. Organized by the UW Institute for Neuroengineering, this year's topics ranged from brain-computer interfaces to rehabilitative robotics and deep learning, with plenary speakers such as Allison Okamura (Stanford), Takaki Komiyama (UC San Diego), Loren Frank (UC San Francisco), Ila Fiete (UT Austin), and David Reinkensmeyer (UC Irvine).
