/Handwritten-Digit-Recoginition

This utilizes the TensorFlow framework to implement a neural network model for handwritten digit recognition. Trained on the MNIST dataset, which consists of 60,000 training images and 10,000 testing images, the model classifies digits from 0 to 9 with high accuracy.

Primary LanguageJupyter NotebookMIT LicenseMIT

Handwritten Digit Recognition with my_model

This repository contains the implementation of a neural network model for handwritten digit recognition. The model architecture includes three dense layers and is designed to classify handwritten digits from the MNIST dataset.

Model Architecture

The architecture of the my_model is as follows:

Layer Output Shape Parameters
Dense (25) (None, 25) 10,025
Dense (15) (None, 15) 390
Dense (10) (None, 10) 160

Total parameters: 10,575 (41.31 KB)
Trainable parameters: 10,575 (41.31 KB)
Non-trainable parameters: 0 (0.00 Byte)

Dataset

The model is trained on the MNIST dataset, which consists of 60,000 training images and 10,000 testing images of handwritten digits from 0 to 9. Each image is 28x28 pixels.

Requirements

To run this project, you need the following libraries:

  • Python 3.x
  • TensorFlow
  • NumPy
  • Matplotlib

License

This project is licensed under the MIT License.

Acknowledgements

The MNIST dataset is provided by Yann LeCun, Corinna Cortes, and Christopher J.C. Burges. TensorFlow for providing the deep learning framework used in this project.