4 min listen
[MINI] Max-pooling
FromData Skeptic
ratings:
Length:
13 minutes
Released:
Jun 2, 2017
Format:
Podcast episode
Description
Max-pooling is a procedure in a neural network which has several benefits. It performs dimensionality reduction by taking a collection of neurons and reducing them to a single value for future layers to receive as input. It can also prevent overfitting, since it takes a large set of inputs and admits only one value, making it harder to memorize the input. In this episode, we discuss the intuitive interpretation of max-pooling and why it's more common than mean-pooling or (theoretically) quartile-pooling.
Released:
Jun 2, 2017
Format:
Podcast episode
Titles in the series (100)
Introduction: The Data Skeptic Podcast features conversations with topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the... by Data Skeptic