This Jupyter Notebook implements a Convolutional Neural Network (CNN) for classifying handwritten digits from the MNIST dataset. The model achieves an accuracy of approximately 98% on the test set.
Key Features:
CNN Architecture: (Replace with specific architecture used, e.g., LeNet-5) Data Preprocessing: Includes techniques like (e.g., normalization) for improved training. Training: Utilizes an efficient optimizer (e.g., Adam) for fast convergence. Notebook:
MNIST_CNN.ipynb: This notebook contains the complete code for building, training, and evaluating the CNN model. Instructions:
Install required libraries (e.g., TensorFlow, NumPy). Open MNIST_CNN.ipynb in a Jupyter Notebook environment. Run all the cells in the notebook to train and evaluate the model. Further Exploration:
This project provides a solid foundation for exploring advanced CNN techniques for digit recognition. Consider:
Experimenting with different CNN architectures within the notebook. Implementing data augmentation techniques within the notebook. Visualizing learned filters to understand the features extracted (code additions required). This Jupyter Notebook offers a user-friendly and customizable CNN solution for MNIST handwritten digit classification.