Monday 20 November 2017
Contact US    |    Archive
6 months ago

MLconf 2017 Seattle Lunch Talk - Using Optimal Learning to tune Deep Learning / AI Models

In this talk we introduce Bayesian Optimization as an efficient way to optimize machine learning model parameters, especially when evaluating different parameters is time-consuming or expensive. Deep learning pipelines are notoriously expensive to train and often have many tunable parameters including hyperparameters, the architecture, feature transformations that can have a large impact on the efficacy of the model. We will motivate the problem by giving several example applications using multiple open source deep learning frameworks and open datasets. We’ll compare the results of Bayesian Optimization to standard techniques like grid search, random search, and expert tuning.

Read on the original site

IMC BA 7014 Unit 3

- slideshare

How to open fd online

- slideshare
Most Popular (6 hours)

Presentation paper 12

- slideshare

Most Popular (24 hours)

Most Popular (a week)


- slideshare

Decreto 2715 de 2010

- slideshare

LRT3 too close for comfort

- themalaymailonline

Palabras desconocidas

- slideshare

Categories - Countries
All News