Wed, 24 Jan 2024
This is a basic project on a neural network trained on the MNIST dataset using Python, Numpy and Matplotlib. This neural network can predict digits with 95% accuracy (when trained with 3000 max iterations and 0.5 learning rate).
Overview
This project is a basic implementation of a neural network trained on the MNIST dataset using Python and Numpy (without TensorFlow). The MNIST dataset is a classic benchmark dataset in the field of machine learning and computer vision. It consists of 28x28 grayscale images of handwritten digits (0-9). The task is to classify these images into their respective digit classes. In this implementation, a simple feedforward neural network is used to classify the images. The architecture of the neural network is as follows:
- Input layer: 784 neurons (28x28 pixels flattened)
- Hidden layer: 10 neurons with ReLU activation
- Output layer: 10 neurons with softmax activation (corresponding to the 10 digit classes)
Acknowledgments
This implementation is based on tutorials and resources from Youtube Channels like 3Blue1Brown, Samson Zhang and other online sources. Feel free to contribute or provide feedback to improve this implementation!