Visual Understanding of Neural Networks | by Reza Bagheri | January, 2025
The math behind neural networks is explained visually
Artificial neural networks are very powerful and at the same time very complex machine learning models. They are especially useful for complex tasks where machine learning algorithms fail. A major advantage of neural networks is their ability to learn complex patterns and relationships in data, even if the data is high dimensional or unstructured.
Many articles discuss the mathematics behind neural networks. Topics such as differential activation functions, forward and backpropagation algorithms, gradient descent, and optimization methods are discussed in detail. In this article, we take a different approach and present a visual understanding of a neural network layer by layer. We will first focus on the physical description of single-layer neural networks for both programming and regression problems and their similarities to other machine learning models. Then we will discuss the importance of hidden layers and indirect activation functions. All visualizations are made using Python.
All images in this article are created by the author.
