This is an example code for the classification of handwritten digits using the MNIST dataset with TensorFlow, NumPy, and Matplotlib.
This project demonstrates the process of building a digit classification model using deep learning techniques. It utilizes TensorFlow, a popular deep learning framework, along with other essential libraries. This project was done to implement the learnings made from the various courses.
The dataset used in this project is the MNIST dataset, a widely used benchmark dataset for handwritten digit recognition tasks.
- Clone this repository to your local machine.
- Ensure you have the necessary dependencies installed, including TensorFlow, NumPy, and Matplotlib.
- Run the provided script or Jupyter Notebook (
number_classification.py) to train the model and evaluate its performance. - The notebook/script includes sections for data loading, preprocessing, model building, training, evaluation, and visualization of results.
- The model is trained on the MNIST dataset and evaluated on a separate test set.
- Evaluation metrics such as loss and accuracy are computed to assess the performance of the model.
- Predictions are made on the test set, and sample predictions are visualized using Matplotlib.