Explore >> Select a destination
|
You are here 
|
polukhin.tech | ||
| | | | |
www.analyticsvidhya.com
|
|
| | | | | Learn about Attention Mechanism, its introduction in deep learning, implementation in Python using Keras, and its applications in computer vision. | |
| | | | |
www.paepper.com
|
|
| | | | | Today's paper: End-to-End object detection with transformers by Carion et al. This is the second paper of the new series Deep Learning Papers visualized and it's about using a transformer approach (the current state of the art in the domain of speech) to the domain of vision. More specifically, the paper is concerned with object detection and here is the link to the paper of Carion et al. on arxiv. | |
| | | | |
machinethink.net
|
|
| | | | | An in-depth look at how fast object detection models are trained | |
| | | | |
www.rdatagen.net
|
|
| | | I've been curious to see how helpful ChatGPT can be for implementing relatively complicated models in R. About two years ago, I described a model for estimating a treatment effect in a cluster-randomized stepped wedge trial. We used a generalized additive model (GAM) with site-specific splines to account for general time trends, implemented using the mgcv package. I've been interested in exploring a Bayesian version of this model, but hadn't found the time to try - until I happened to pose this simple question to ChatGPT: | ||
