Explore >> Select a destination
|
You are here 
|
blog.evjang.com | ||
| | | | |
iclr-blogposts.github.io
|
|
| | | | | The product between the Hessian of a function and a vector, the Hessian-vector product (HVP), is a fundamental quantity to study the variation of a function. It is ubiquitous in traditional optimization and machine learning. However, the computation of HVPs is often considered prohibitive in the context of deep learning, driving practitioners to use proxy quantities to evaluate the loss geometry. Standard automatic differentiation theory predicts that the computational complexity of an HVP is of the same order of magnitude as the complexity of computing a gradient. The goal of this blog post is to provide a practical counterpart to this theoretical result, showing that modern automatic differentiation frameworks, JAX and PyTorch, allow for efficient computat... | |
| | | | |
questionableengineering.com
|
|
| | | | | John W Grun AbstractIn this paper, a manually implemented LeNet-5 convolutional NN with an Adam optimizer written in Numpy will be presented. This paper will also cover a description of the data use | |
| | | | |
kevinlynagh.com
|
|
| | | | | ||
| | | | |
aimatters.wordpress.com
|
|
| | | A few weeks ago, it was announced that Keras would be getting official Google support and would become part of the TensorFlow machine learning library. Keras is a collectionof high-level APIs in Python for creating and training neural networks, using either Theano or TensorFlow as the underlying engine. Given my previous posts on implementing an... | ||
