This repository contains a Python library for building simple Deep Neural Networks from scratch, using only vectorized operations with NumPy.
The goal of this project is to develop further understanding in the inner workings of neural networks and provide a foundation for building a Production-ready Python library.
The current version of the library includes the following features:
- A customizable neural network architecture with support for multiple layers and activation functions
- Stochastic gradient descent optimization algorithm with support for different loss functions [comment]<> : (
- A suite of evaluation metrics, such as accuracy and mean squared error, for measuring model performance
- A suite of utilities for data preprocessing, including normalization and one-hot encoding )
To use this library, you need to install it on your system. You can install it by running
pip install --no-cache-dir .Once you have it installed, you can import the library into your Python code using:
from neural_network import DeepNNFrom there, you can create an instance of the Deep Neural Network architecture as a python class and customize it to fit your needs. For example, to create a neural network with one hidden layer and a sigmoid activation function, you can use the following code:
nn = DeepNN(layers_dims=[input_size, hidden_size, output_size], activations=["sigmoid", "sigmoid"])This library is a work in progress, and there are several areas where you can contribute and improve its functionality. Here are some ideas:
- Optimizing performance: While the library is currently optimized for efficiency using NumPy, you can try using a GPU-accelerated computing library like CuPy and CuPyx to further improve its performance.
- Developing new architectures of NNs: The current library supports only a simple feedforward neural network architecture, but you can explore other architectures, such as convolutional neural networks or recurrent neural networks, and implement them using the existing framework.
- Adding more evaluation metrics: The library currently supports a limited set of evaluation metrics. You can add more metrics, such as precision and recall, to provide a more comprehensive view of model performance.
If you are interested in contributing to this project, please check out the contribution guidelines for more information on how to get started.
This project is licensed under the MIT License.
