Typically you don’t want functions that take a lot of parameters, and though there’s no magic number for how many is “too many”as it depends a bit on what the function is doing. But when you have a function that takes many parameters, there’s a good chance that the function is not exactly “Single Responsibility” and is doing too much.
Source: Go: Reduce function parameters, an article by Dylan Meeus.
Proponents of Bayesian neural networks often claim that trained BNNs output distributions which capture epistemic uncertainty. Epistemic uncertainty is incredibly valuable for a wide variety of applications, and we agree with the Bayesian approach in general. However, we argue that BNNs require highly informative priors to handle uncertainty. We show that if the prior does not distinguish between functions that generalize and functions that don’t, Bayesian inference cannot provide useful uncertainties. This puts into question the standard argument that “uninformative priors” are appropriate when the true prior distribution is unknown.
Source: A Sober Look at Bayesian Neural Networks, an article by Jacob Buckman.
In a fearsome new world where Obsidian pirates roam the Belt, famine and genocide ravage Mars, and crime lords terrorise Luna, it's time for Darrow and a cast of new characters from across the solar system to face down the chaos that revolution has unleashed.
In the evening I started in Iron Gold, the fourth book in the Red Rising series by Pierce Brown. I read the first 3 books some time ago so it took me some effort to get into this book. From the previous three I liked the first book, Red Rising, the most.
Ever since she spotted the stone chairs from the bus in Delft, Alice wanted to sit in one. And today, when she left school she had the opportunity.
I saw two Egyptian geese standing near the edge of a small canal. I used my iPhone 5 to take a few photos.
Many people manage their tasks using a to-do list. Everything they need need to work on at some point ends up on that list, and they measure their productivity by looking at the number of tasks completed in a certain amount of time. While I’m a big fan of checklists—which have a clear objective—I don’t think to-do lists should be managed the same way a shopping list would. It’s great to have a place to dump all your tasks in, but how do you decide what to work on next?
Source: How to decide what to work on next, an article by Anne-Laure Le Cunff.
On our way home from Delft, Alice and I admired a beautiful sunset from the bus.
Python haters always say, that one of reasons they don't want to use it, is that it's slow. Well, whether specific program - regardless of programming language used - is fast or slow is very much dependant on developer who wrote it and their skill and ability to write optimized and fast programs.
So, let's prove some people wrong and let's see how we can improve performance of our Python programs and make them really fast!
Source: Making Python Programs Blazingly Fast, an article by Martin Heinz.
In the morning after Esme and I had dropped Alice at her school we went for a short walk in the city of Delft, where the school is located. On the way back to the bus stop we bought a Jewel Orchid, Ludisia discolor, we had seen on the way to the school.
Currently the orchid is keeping the Chamaedorea elegans we bought earlier this month company.
In the late afternoon I finished Neon Prey by John Sandford. A lot of action; an excellent read.