11 Essential Neural Network Architectures, Visualized & Explained
Standard, Recurrent, Convolutional, & Autoencoder Networks
With the rapid development of deep learning, an entire host of neural network architectures have been created to address a wide variety of tasks and problems. Although there are countless neural network architectures, here are eleven that are essential for any deep learning engineer to understand, split into four general categories: the standard networks, the recurrent networks, the convolutional networks, and the autoencoders.
All diagrams created by Author.
The Standard Networks
1 | The Perceptron
The perceptron is the most basic of all neural networks, being a fundamental building block of more complex neural networks. It simply connects an input cell and an output cell.
2 | The Feed-Forward Network
The feed-forward network is a collection of perceptrons, in which there are three fundamental types of layers — input layers, hidden layers, and output layers. During each…
