Explore >> Select a destination
|
You are here 
|
martinapugliese.github.io | ||
| | | | |
markodenic.com
|
|
| | | | | Free programming books, algorithms, public APIs, and much more. | |
| | | | |
initialcommit.com
|
|
| | | | | "Artificial Intelligence: A Modern Approach is a book for learning Artificial Intelligence, learning Machine Learning, learning Graph theory and other related topics. It has core concepts for building AI agents, and offers an extensive guide into probability and statistics, plus other topics related to AI." | |
| | | | |
futurism.com
|
|
| | | | | A team of scientists uncovered how our brains process 3D images - then discovered that neural networks happen to do it the same way. | |
| | | | |
www.paepper.com
|
|
| | | Today's paper: Rethinking 'Batch' in BatchNorm by Wu & Johnson BatchNorm is a critical building block in modern convolutional neural networks. Its unique property of operating on "batches" instead of individual samples introduces significantly different behaviors from most other operations in deep learning. As a result, it leads to many hidden caveats that can negatively impact model's performance in subtle ways. This is a citation from the paper's abstract and the emphasis is mine which caught my attention. Let's explore these subtle ways which can negatively impact your model's performance! The paper of Wu & Johnson can be found on arxiv. | ||
