Overview of Neural Networks: Exploration of Architectures and Implementations

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Neural networks come in various architectures, each designed for specific tasks.

I. Types of Neural Networks:

1. Feedforward Neural Network (FNN):

• The simplest type of neural network where information travels in one direction, from input to output.

2. Multilayer Perceptron (MLP):

• A type of feedforward neural network with multiple layers (input layer, hidden layers, output layer) that can learn complex patterns.

3. Radial Basis Function Neural Network (RBFNN):

• Utilizes radial basis functions as activation functions in the hidden layer and is often used for pattern recognition.

4. Convolutional Neural Network (CNN):

• Designed for image processing and pattern recognition, it uses convolutional layers to automatically and adaptively learn spatial hierarchies of features.

5. Recurrent Neural Network (RNN):

• Contains connections that form directed cycles, allowing it to maintain a memory of previous inputs. Commonly used for…