/explore

Click through on any links that interest you or select the planets on the right to continue exploring the Outer Web.
You are here

fa.bianp.net
| | francisbach.com
1.0 parsecs away

Travel
| | [AI summary] This text discusses the scaling laws of optimization in machine learning, focusing on asymptotic expansions for both strongly convex and non-strongly convex cases. It covers the derivation of performance bounds using techniques like Laplace's method and the behavior of random minimizers. The text also explains the 'weird' behavior observed in certain plots, where non-strongly convex bounds become tight under specific conditions. The analysis connects theoretical results to practical considerations in optimization algorithms.
| | www.randomservices.org
1.6 parsecs away

Travel
| | [AI summary] The text covers various topics in probability and statistics, including continuous distributions, empirical density functions, and data analysis. It discusses the uniform distribution, rejection sampling, and the construction of continuous distributions without probability density functions. The text also includes data analysis exercises involving empirical density functions for body weight, body length, and gender-specific body weight.
| | dustintran.com
2.4 parsecs away

Travel
| | Stochastic gradient descent (SGD) has seen wide application for learning problems on large scale data, whether this be for generalized linear models [6], SVM...
| | coornail.net
12.2 parsecs away

Travel
| Neural networks are a powerful tool in machine learning that can be trained to perform a wide range of tasks, from image classification to natural language processing. In this blog post, well explore how to teach a neural network to add together two numbers. You can also think about this article as a tutorial for tensorflow.