This repository contains a simple implementation of a feedforward neural network using Python and NumPy. The neural network is designed to have customizable layers with sigmoid activation functions.

• neural_network.py: Contains the implementation of the NeuralNetwork class, including methods for creating layers, performing forward propagation, and applying the sigmoid activation function.
• main.py: Demonstrates how to use the NeuralNetwork class to create a neural network, define layers, provide input data, and obtain the final output.

• Python 3.x
• NumPy

1. Clone or download the repository to your local machine.
2. Ensure you have Python and NumPy installed.
3. Open a terminal or command prompt and navigate to the directory containing the files.

To run the example provided in main.py:

python main.py

You can customize the neural network by modifying the weights, biases, number of layers, and activation functions in the main.py file. Additionally, you can explore the NeuralNetwork class in neural_network.py to understand how the network is structured and make further modifications as needed.