Explore >> Select a destination
|
You are here 
|
francisbach.com | ||
| | | | |
www.khanna.law
|
|
| | | | | You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now? | |
| | | | |
michael-lewis.com
|
|
| | | | | This is a short summary of some of the terminology used in machine learning, with an emphasis on neural networks. I've put it together primarily to help my own understanding, phrasing it largely in non-mathematical terms. As such it may be of use to others who come from more of a programming than a mathematical background. | |
| | | | |
fa.bianp.net
|
|
| | | | | The Langevin algorithm is a simple and powerful method to sample from a probability distribution. It's a key ingredient of some machine learning methods such as diffusion models and differentially private learning. In this post, I'll derive a simple convergence analysis of this method in the special case when the ... | |
| | | | |
kavita-ganesan.com
|
|
| | | This article examines the parts that make up neural networks and deep neural networks, as well as the fundamental different types of models (e.g. regression), their constituent parts (and how they contribute to model accuracy), and which tasks they are designed to learn. | ||
