Explore >> Select a destination
|
You are here 
|
francisbach.com | ||
| | | | |
blog.ml.cmu.edu
|
|
| | | | | The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University. | |
| | | | |
www.ethanepperly.com
|
|
| | | | | ||
| | | | |
nickhar.wordpress.com
|
|
| | | | | 1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B... | |
| | | | |
dennybritz.com
|
|
| | | Recurrent Neural Networks (RNNs) are popular models that have shown great promise in manyNLP tasks. | ||
