Explore >> Select a destination
|
You are here 
|
www.markhw.com | ||
| | | | |
www.johnnylogic.org
|
|
| | | | | [AI summary] This blog post introduces network science concepts, covering graph types, generative models, analysis methods, and applications like community detection and network diffusion. | |
| | | | |
www.jeremykun.com
|
|
| | | | | Graphs are among the most interesting and useful objects in mathematics. Any situation or idea that can be described by objects with connections is a graph, and one of the most prominent examples of a real-world graph that one can come up with is a social network. Recall, if you aren't already familiar with this blog's gentle introduction to graphs, that a graph $ G$ is defined by a set of vertices $ V$, and a set of edges $ E$, each of which connects two vertices. | |
| | | | |
cyclostationary.blog
|
|
| | | | | Cross correlation functions can be normalized to create correlation coefficients. The spectral correlation function is a cross correlation and its correlation coefficient is called the coherence. | |
| | | | |
scorpil.com
|
|
| | | In Part One of the "Understanding Generative AI" series, we delved into Tokenization - the process of dividing text into tokens, which serve as the fundamental units of information for neural networks. These tokens are crucial in shaping how AI interprets and processes language. Building upon this foundational knowledge, we are now ready to explore Neural Networks - the cornerstone technology underpinning all Artificial Intelligence research. A Short Look into the History Neural Networks, as a technology, have their roots in the 1940s and 1950s. | ||
