27 min listen
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
LM101-030: How to Improve Deep Learning Performance with Artificial Brain Damage (Dropout and Model Averaging)
ratings:
Length:
32 minutes
Released:
Jun 8, 2015
Format:
Podcast episode
Description
Deep learning machine technology has rapidly developed over the past five years due in part to a variety of actors such as: better technology, convolutional net algorithms, rectified linear units, and a relatively new learning strategy called "dropout" in which hidden unit feature detectors are temporarily deleted during the learning process. This article introduces and discusses the concept of "dropout" to support deep learning performance and makes connections of the "dropout" concept to concepts of regularization and model averaging. For more details and background references, check out: www.learningmachines101.com !
Released:
Jun 8, 2015
Format:
Podcast episode
Titles in the series (85)
LM101-022: How to Learn to Solve Large Constraint Satisfaction Problems: In this episode we discuss how to learn to solve constraint satisfaction inference problems. by Learning Machines 101