Explore >> Select a destination
|
You are here 
|
vankessel.io | ||
| | | | |
www.hhyu.org
|
|
| | | | | Science, programming, books, and other interesting stuff | |
| | | | |
www.reedbeta.com
|
|
| | | | | When you read BRDF theory papers, you'll often see mention of slope space. Sometimes, components of the BRDF such as NDFs or masking-shadowing functions are defined in slope space, or operations are done in slope space before being converted back to ordinary vectors or polar coordinates. However, the meaning and intuition of slope space is rarely explained. Since it may not be obvious exactly what slope space is, why it is useful, or how to transform things to and from it, I thought I would write down a ... | |
| | | | |
jaydaigle.net
|
|
| | | | | We continue our exploration of what numbers are, and where mathematicians keep finding weird ones. In the first three parts we extended the natural numbers in two ways: algebraically and analytically. Those approaches gave overlapping but distinct sets of numbers. This week we combine them to get the complex numbers, and see some hints of why the complex numbers are so useful-and so frustrating. | |
| | | | |
blog.otoro.net
|
|
| | | [AI summary] This article describes a project that combines genetic algorithms, NEAT (NeuroEvolution of Augmenting Topologies), and backpropagation to evolve neural networks for classification tasks. The key components include: 1) Using NEAT to evolve neural networks with various activation functions, 2) Applying backpropagation to optimize the weights of these networks, and 3) Visualizing the results of the evolved networks on different datasets (e.g., XOR, two circles, spiral). The project also includes a web-based demo where users can interact with the system, adjust parameters, and observe the evolution process. The author explores how the genetic algorithm can discover useful features (like squaring inputs) without human intervention, and discusses the ... | ||
