This project aims to build and train a neural network model to accurately predict handwritten digits from the MNIST dataset. The MNIST dataset, which stands for Modified National Institute of Standards and Technology database, is a large collection of handwritten digits that is commonly used for training various image processing systems. The repository contains a Jupyter Notebook, MNISTPredictionsWithNN.ipynb, detailing the entire process from loading the data, preprocessing, model creation, training, and evaluation.
The goal of this project is to demonstrate the effectiveness of neural networks in recognizing and predicting handwritten digits. By leveraging the MNIST dataset, this project explores the capabilities of deep learning models to perform image classification tasks with high accuracy.
Data Exploration: Initial exploration of the MNIST dataset to understand its structure and composition. Data Preprocessing: Preparation of the dataset for training, including normalization and reshaping of images. Model Building: Construction of a neural network model using TensorFlow and Keras. Model Training: Training the model on the MNIST dataset with appropriate hyperparameters. Evaluation: Assessing the model's performance using metrics such as accuracy and loss, and visualizing predictions on test images. Insights: Discussion of the results, including potential areas for improvement and further exploration.
Python: The primary programming language for the project. TensorFlow and Keras: For building and training the neural network model. Matplotlib: For visualization of data and results. NumPy: For numerical and matrix operations on data.
To replicate and explore this project, follow these steps:
Clone the Repository: git clone https://github.com/CodCodingCode/MNISTPredictions.git
Navigate to the Project Directory: cd MNIST-Predictions-with-NN
Install Required Libraries: Ensure you have Python installed, then install the required libraries. pip install tensorflow numpy matplotlib
Launch the Notebook: jupyter notebook MNISTPredictionsWithNN.ipynb
Follow the instructions and code in the notebook for a detailed walkthrough of the project.
Contributions are welcome! If you have suggestions for improvements, feel free to fork the repository, make your changes, and submit a pull request. For major changes or questions, please open an issue first to discuss what you would like to change.
This project is licensed under the MIT License - see the LICENSE file for details.