How Do Convolutional Layers Work in Deep Learning Neural Networks?

Last Updated on April 17, 2020

Convolutional layers are the major building blocks used in convolutional neural networks.

A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input, such as an image.

The innovation of convolutional neural networks is the ability to automatically learn a large number of filters in parallel specific to a training dataset under the constraints of a specific predictive modeling problem, such as image classification. The result is highly specific features that can be detected anywhere on input images.

In this tutorial, you will discover how convolutions work in the convolutional neural network.

After completing this tutorial, you will know:

• Convolutional neural networks apply a filter to an input to create a feature map that summarizes the presence of detected features in the input.
• Filters can be handcrafted, such as line detectors, but the innovation of convolutional neural networks is to learn the filters during training in the context of a specific prediction problem.
• How to
  To finish reading, please visit source site