/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. | |
| | | | |
www.jeremymorgan.com
|
|
| | | | | Want to learn about PyTorch? Of course you do. This tutorial covers PyTorch basics, creating a simple neural network, and applying it to classify handwritten digits. | |
| | | | |
www.nicktasios.nl
|
|
| | | | | In the Latent Diffusion Series of blog posts, I'm going through all components needed to train a latent diffusion model to generate random digits from the MNIST dataset. In this first post, we will tr | |
| | | | |
www.lesswrong.com
|
|
| | | A neural net using rectified linear unit activation functions of any size is unable to approximate the function sin(x) outside a compact interval. ... | ||
