You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Many of the answers here are really, really good. But the OpenGL and Direct3D (D3D) issue should probably be addressed. And that requires... a history lesson.
And before we begin, I know far more about OpenGL than I do about Direct3D. I've never written a line of D3D code in my life, and I've written tutorials on OpenGL. So what I'm about to say isn't a question of bias. It is simply a matter of history.
Birth of Conflict
One day, sometime in the early 90's, Microsoft looked around. They saw the SNES and Sega Genesis being awesome, running lots of action games and such. And they saw DOS. Developers coded DOS games like console games: direct to the metal. Unlike consoles however, where a developer who made an SNES game knew what hardware the user would have, DOS developers had to write for multiple possible configurations. And this is rather harder than it sounds.
GLFW windows by default use double buffering. That means that each window has two rendering buffers; a front buffer and a back buffer. The front buffer is the one being displayed and the back buffer the one you render to.
When the entire frame has been rendered, the buffers need to be swapped with one another, so the back buffer becomes the front buffer and vice versa.
I frequently deal with collections of things in the programs I write. Collections of droids, jedis, planets, lightsabers, starfighters, etc. When programming in Python, these collections of things are usually represented as lists, sets and dictionaries. Oftentimes, what I want to do with collections is to transform them in various ways. Comprehensions is a powerful syntax for doing just that. I use them extensively, and it's one of the things that keep me coming back to Python. Let me show you a few examples of the incredible usefulness of comprehensions.
A Few Useful Things to Know about Machine Learning
The paper presents some key lessons and "folk wisdom" that machine learning researchers and practitioners have learnt from experience and which are hard to find in textbooks.
All machine learning algorithms have three components:
Representation for a learner is the set if classifiers/functions that can be possibly learnt. This set is called hypothesis space. If a function is not in hypothesis space, it can not be learnt.
Evaluation function tells how good the machine learning model is.
Optimisation is the method to search for the most optimal learning model.
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Install node-js, npm and yarn on Ubuntu/Mac using nvm
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters