/explore
|
You are here 
|
www.paepper.com | ||
| | | | |
coen.needell.org
In my last post on computer vision and memorability, I looked at an already existing model and started experimenting with variations on that architecture. The most successful attempts were those that use Residual Neural Networks. These are a type of deep neural network built to mimic specific visual structures in the brain. ResMem, one of the new models, uses a variation on ResNet in its architecture to leverage that optical identification power towards memorability estimation. |
|
| | | | | ||
| | | | |
towardsdatascience.com
Learn how to build feedforward neural networks that are interpretable by design using PyTorch. |
|
| | | | | ||
| | | | |
sebastianraschka.com
I'm Sebastian: a machine learning & AI researcher, programmer, and author. As Staff Research Engineer Lightning AI, I focus on the intersection of AI research, software development, and large language models (LLMs). |
|
| | | | | ||
| | | | |
www.chrisritchie.org
[AI summary] The post documents a web-based simulation of artificial life agents controlled by evolving neural networks that learn to eat, swarm, and reproduce within a basic environment. |
|
| | | |||
