Abstract
Mnist Handwritten Digit Recognition is an open-source AI & Machine Learning project. Keras Fully Connected Neural Network using Python for Digit Recognition. About Welcome to another tutorial on Keras. This tutorial will be exploring how to build a Fully Connected Neural Network model for Object Classification on Mnist Dataset. It is built using Jupyter Notebook, Keras, Python. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.
1. Introduction
About Welcome to another tutorial on Keras. This tutorial will be exploring how to build a Fully Connected Neural Network model for Object Classification on Mnist Dataset. Let's get straight into it!
The MNIST database of handwritten digits, has a training set of 60,000 examples, and a test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image.
It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting.
2. Objective
Keras Fully Connected Neural Network using Python for Digit Recognition
This project demonstrates how Jupyter Notebook, Keras, Python can be applied to a real-world AI & Machine Learning problem.
4. Technology Stack
5. System Requirements
General requirements for this technology stack — check the README for exact versions.
- Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- Python 3.8 or later
- pip / virtualenv for dependencies
- VS Code, PyCharm or Jupyter Notebook
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/husnainfareed/mnist-handwritten-digit-recognition.git
cd mnist-handwritten-digit-recognitionFull setup instructions are in the project README.
7. Future Enhancements
Suggested extensions you can add to make this your own project.
- Deploy the model as a web app with Streamlit, Flask or FastAPI
- Compare against an additional model and report the metric difference
- Add explainability (SHAP / Grad-CAM)
8. Viva / Review Questions
Common questions examiners ask for projects in this domain.
- What dataset does the project use and how was it pre-processed?
- Which algorithm / model architecture is used and why was it chosen over alternatives?
- How are training and testing data split, and how is overfitting avoided?
- Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
- How would you deploy this model for real users?
9. Source Code & License
This project is developed by husnainfareed and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.
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