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This Jupyter Notebook demonstrates a TensorFlow model for recognizing handwritten digits using the MNIST dataset, focusing on model construction, training, and accuracy evaluation.
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This repository contains a Jupyter Notebook that demonstrates the use of TensorFlow to recognize handwritten digits from the MNIST dataset. The project utilizes a convolutional neural network (CNN) for high accuracy in digit classification.
To get started with this project, clone the repository to your local machine:
git clone https://github.com/your-username/mnist-digit-recognition.
jupyter notebook MNIST_Digit_Recognition.ipynb
The MNIST dataset comprises 70,000 grayscale images of handwritten digits (0-9), each of size 28x28 pixels. The dataset is split into 60,000 training images and 10,000 testing images.
The notebook details the CNN architecture used for digit classification, including layers, activation functions, and compilation strategy.
Evaluation metrics are provided within the notebook to assess the accuracy and effectiveness of the trained model on test data.
Contributions to this project are welcome. Please fork the repository and submit pull requests to enhance the functionality or performance of the model.
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