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Understanding Deep Learning Applications in Rare Event Prediction

Architecture of Convolutional Networks

Explore the architecture of convolutional neural networks including convolutional layers, filters, pooling, and output flattening. Learn how Conv1D, Conv2D, and Conv3D layers apply to different data types and how to calculate output sizes essential for building effective deep learning models.

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Structure

This section exemplifies the structure of a convolutional network. The illustration below shows an elementary convolutional network.

Convolutional network
Convolutional network

The components of the network are as follows:

  • Grid-like input: Convolutional layers take grid-like inputs. The input in the illustration is like an image, that is, it has two axes and three channels each for blue\color{blue}{\text{blue}}, red\color{red}{\text{red}} ...