Explore >> Select a destination
|
You are here 
|
blog.demofox.org | ||
| | | | |
www.johndcook.com
|
|
| | | | | The most obvious way to compute the soft maximum can easily fail due to overflow or underflow. The soft maximum of x and y is defined by g(x, y) = log( exp(x) + exp(y) ). The most obvious way to turn the definition above into C code would be double SoftMaximum(double x, double y) { | |
| | | | |
piware.de
|
|
| | | | | ||
| | | | |
studywolf.wordpress.com
|
|
| | | | | In working towards reproducing some results fromdeep learning control papers, one of the learning algorithms that came up was natural policy gradient. The basic idea of natural policy gradient is to use the curvature information of the of the policy's distribution over actions in the weight update. There are good resources that go into details... | |
| | | | |
sebastianraschka.com
|
|
| | | The PyTorch team recently announced TorchData, a prototype library focused on implementing composable and reusable data loading utilities for PyTorch. In... | ||
