10 Illustrations for Neural Network Model Architectures + Key Characteristics and Use Cases

Photo by Google DeepMind on Unsplash

Let’s explore several types of neural network models, along with key characteristics and use cases for each. Please note that this is not an exhaustive list, but it covers some of the most commonly used neural network architectures:

1. Feedforward Neural Network (FNN):

Input Layer         Hidden Layer        Output Layer
+-----------+       +------------+      +------------+
| Input 1   |       | Neuron 1   |      | Output 1   |
| Input 2   |       | Neuron 2   |      +------------+
|   ...     |       |    ...     |      | Output 2   |
| Input N   |       | Neuron M   |      +------------+
+-----------+       +------------+

• Input values are fed into the input layer.
• Weights are applied to connections, and a weighted sum is calculated for each neuron in the hidden layer.
• An activation function is applied to the weighted sum, producing the output of each hidden layer neuron.
• The process is repeated for subsequent hidden layers until the final output layer is reached.
• The final outputs represent the predictions of the neural network.

i. Characteristics: