Neural Networks in Numpy

This project is an implementation of a neural network from scratch. The goal is to build a functional neural network library using only NumPy, for educational purposes.

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Current Features:

• Dense Layer: A fully connected layer with forward and backward propagation.
• Activation Functions: ReLU, Sigmoid, Softmax, and Linear activations with backward pass.
• Loss Functions: MSE, MAE, SSE, Categorical Cross-Entropy, and Sparse Cross-Entropy.
• Backpropagation: Full implementation of the backward pass.
• Optimizers: An optimizer base class with an initial SGD (Stochastic Gradient Descent) implementation.
• Model API: A Model class to easily build, compile, and train neural networks.
• He initialization: Improve weight initialization for deeper networks.

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Future Goals:

• Advanced Optimizers: Implement more sophisticated optimizers like Adam and RMSprop.
• Batch & Data Handling: Support for mini-batch training and data shuffling for more efficient training on large datasets.
• Metrics & Callbacks: Include accuracy metrics during training and a callback system for actions like early stopping.
• Advanced Layer Types: Add more complex layers, such as Convolutional and Pooling layers.
• Miscellaneous: Code refactoring and improvements, better data handling and easier compatibility with pandas

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Setup Instructions

1. Clone the Repository:

git clone https://github.com/Awesome075/Neural-Networks-Numpy-.git
cd Neural-Networks-Numpy-

2. Create a virtual environment:

python -m venv venv

3. Activate the Environment

   • On Windows:

      venv\Scripts\activate

   • On Linux/macOS:

      source venv/bin/activate

4. Install Dependencies:

pip install -r requirements.txt

5. Run the Main example:

python main.py

License

This project is licensed under the MIT License - see the LICENSE file for details.