8 Common Types of Neural Networks: An Overview of Characteristics, Strengths, Weaknesses, and Use Cases

Photo by Pawel Czerwinski on Unsplash

Neural networks are a class of machine learning models inspired by the structure and function of the human brain. They consist of interconnected nodes (artificial neurons) organized into layers. Each connection between nodes has a weight, and the model learns to adjust these weights during training to make predictions or decisions based on input data. Here’s a comprehensive overview of neural networks:

I. Basic Components:

1. Neurons:

• The fundamental units of a neural network, mimicking the neurons in the human brain.
• Each neuron receives input, applies a weighted sum, adds a bias, and passes the result through an activation function to produce an output.

2. Layers:

• Neural networks are organized into layers:
• Input Layer: Receives the initial data.
• Hidden Layers: Intermediate layers that process information.
• Output Layer: Produces the final output.

3. Weights and Biases:

• Weights determine the strength of connections between neurons.
• Biases provide an…