Explore >> Select a destination
|
You are here 
|
blog.paperspace.com | ||
| | | | |
www.analyticsvidhya.com
|
|
| | | | | Learn computer vision with the collection of the top resources for computer vision. This learning path is helpful to master computer vision. | |
| | | | |
www.paperspace.com
|
|
| | | | | Launch a GPU-enabled Jupyter Notebook from your browser in seconds. Notebooks do not require any setup or management of servers or dependencies. | |
| | | | |
sander.ai
|
|
| | | | | Slides for my talk at the Deep Learning London meetup | |
| | | | |
www.fromthebottomoftheheap.net
|
|
| | | [AI summary] The text discusses the use of generalized additive models (GAMs) to represent random effects as smooths, enabling the testing of random effects against a null of zero variance. It compares this approach with traditional mixed-effects models (e.g., lmer) and highlights the advantages and limitations of each. Key points include: (1) Representing random effects as smooths in GAMs allows for efficient testing of variance components and compatibility with complex distributional models. (2) While GAMs can fit such models, they are computationally slower for large datasets with many random effects due to the lack of sparse matrix optimization. (3) The AIC values for models with and without random effects are similar, suggesting that the simpler model i... | ||
