Neural Network from Scratch with NumPy

Overview This project involves building a neural network from scratch using NumPy. The neural network is designed for classification tasks and includes functionalities for forward propagation, backward propagation, training, prediction, and visualization.

Features Customizable Architecture: Define the neural network structure with any number of layers and nodes. Activation Functions: Includes ReLU and Sigmoid activation functions. Training: Implements forward and backward propagation for training the network. Visualization: Visualize the neural network architecture using Matplotlib. Directory Structure:

├── neuro.py # Implementation of the NeuralNetwork class ├── image.py # Basic script to visualize the neural network ├── image2.py # Improved script to visualize the neural network with large nodes ├── test.py # Basic script to test the neural network ├── test2.py # Improved script with if __name__ == "__main__" ├── notes.txt # Project notes └── README.md # Project README file

Usage Testing the Neural Network:

1- The test.py script demonstrates training the neural network with a simple XOR problem. For an improved version with the if __name__ == "__main__" clause, see test2.py.

Visualizing the Neural Network:

2- The image.py script visualizes the neural network architecture. For an improved version that handles large numbers of nodes in hidden layers, see image2.py.

Examples:

test.py and test2.py outputs:

The best result:

Project Notes Detailed notes about the project's design, features, challenges, and solutions are documented in notes.txt.

Future Enhancements Additional Activation Functions: Implement more activation functions such as Tanh, Leaky ReLU, etc. Regularization Techniques: Add regularization methods to prevent overfitting. Advanced Optimizers: Implement advanced optimization algorithms like Adam, RMSprop, etc. Hyperparameter Tuning: Provide a mechanism for automated hyperparameter tuning. Acknowledgements Inspired by various online tutorials and documentation on neural networks and machine learning. Thanks to the open-source community for providing the tools and libraries that made this project possible.