People are companies’ most important assets. We’ve all known this for a long time, but 1) we pay it lip service more often than we try to do something about it, and 2) it’s true more now than ever.

The rise of technology and the information age has resulted in more companies that compete based primarily on their people. This isn’t only true for technology companies like Facebook and Google; as software continues to eat the world and the pace of business increases, nearly all companies will live and die by their continual ability to innovate.

Despite the fact that most organizations know that their long term advantage resides in their people, most companies don’t think critically about how to increase employee retention.

In this post, I’ll argue that the core reason people don’t think about employee retention seriously enough is because they don’t know how to measure the impact. I’ll then share some frameworks for how you might associate dollar values with regrettable turnover, and once I’ve (hopefully) convinced you that this matters, give you some actionable ideas for improving the state of affairs.

Source: How much does employee turnover really cost? – Resources for Humans

Source: VR

Darwin himself was careful to tiptoe around the implications of his theory. He wanted to spend his time thinking about biology, not arguing with people who accused him of being an atheist.

Paul wrote an essay called “What You Can’t Say.” In it he said:

The trouble with keeping your thoughts secret, though, is that
you lose the advantages of discussion. Talking about an idea leads
to more ideas. So the optimal plan, if you can manage it, is to
have a few trusted friends you can speak openly to.

In my blog post, “Subtle Mid-Stage Startup Pitfalls” I said:

You can’t prevent yourself from being a target. It’s an automatic
consequence of being successful. So the best you can do is react
in the right way when people attack you. To some extent you have
to resign yourself to letting people lie about you.

Source: The Sound of Silence – Jessica Livingston

Healthcare
We are excited by the potential to use machine learning to augment the abilities of doctors and healthcare practitioners. As just one example of the possibilities, in a paper published in the Journal of the American Medical Association (JAMA), we demonstrated that a machine-learning driven system for diagnosing diabetic retinopathy from a retinal image could perform on-par with board-certified ophthalmologists. With more than 400 million people at risk for blindness if early symptoms of diabetic retinopathy go undetected, but too few ophthalmologists to perform the necessary screening in many countries, this technology could help ensure that more people receive the proper screening. We are also doing work in other medical imaging domains, as well as investigating the use of machine learning for other kinds of medical prediction tasks. We believe that machine learning can improve the quality and efficiency of the healthcare experience for doctors and patients, and we’ll have more to say about our work in this area in 2017.
Source: Research Blog: The Google Brain team — Looking Back on 2016

There are already a fair number of books about Numpy (see Bibliography) and a legitimate question is to wonder if another book is really necessary. As you may have guessed by reading these lines, my personal answer is yes, mostly because I think there is room for a different approach concentrating on the migration from Python to Numpy through vectorization. There are a lot of techniques that you don’t find in books and such techniques are mostly learned through experience. The goal of this book is to explain some of these techniques and to provide an opportunity for making this experience in the process.

Website: http://www.labri.fr/perso/nrougier/from-python-to-numpy

Disclaimer: All external pictures should have associated credits. If there are missing credits, please tell me, I will correct it. Similarly, all excerpts should be sourced (wikipedia mostly). If not, this is an error and I will correct it as soon as you tell me.

Source: From Python to Numpy

The differences between system administrators and software developers as well as why all software developers should have some amount of sysadmin experience.

Source: Professor Beekums: Software Developers Should Have Sysadmin Experience