💻 Project Hardware/Software
Python source code (Jupyter notebook or .py file)
Trained CNN model and saved weights
Setup instructions and dependencies file (requirements.txt)📄 Project Report
Detailed documentation covering dataset, CNN architecture, results, and conclusions.🎥 Project Demo
Video demonstration showing training, evaluation, and prediction on digit images.🚚 Free Delivery
Complete project resources delivered in a downloadable format.💬 Enhanced Customer Support
Support for installation, model tuning, and extending the project features.
Project Overview
The MNIST dataset is one of the most widely used datasets in the field of machine learning and computer vision. It contains 70,000 grayscale images of handwritten digits (0–9), divided into 60,000 training and 10,000 testing samples. Each image is 28x28 pixels in size and represents a single digit.
In this project, a Convolutional Neural Network (CNN) is developed using Python and TensorFlow to classify these handwritten digits. The process includes data preprocessing, model architecture design, training, evaluation, and digit prediction. The CNN is trained on the training dataset and tested on unseen data to evaluate its performance using metrics like accuracy and confusion matrix.
This project serves as an ideal starting point for students and beginners to understand the fundamentals of deep learning, particularly in the area of image classification. It also introduces core concepts like activation functions, loss functions, dropout regularization, and model optimization.
Project Objectives
Data Loading and Preprocessing
Load the MNIST dataset using TensorFlow/Keras.
Normalize image pixel values and reshape input data as required by the CNN.CNN Architecture Design
Create a deep learning model with:
Convolutional layers
Pooling layers
Fully connected layers
Activation functions (e.g., ReLU, softmax)
Apply dropout to prevent overfitting.Model Training and Validation
Compile the model with an appropriate optimizer and loss function.
Train the model using the training data and validate it on the test set.
Plot training/validation accuracy and loss graphs.Model Evaluation and Testing
Evaluate model performance using:
Accuracy
Confusion matrix
Classification report
Test the model with custom or new digit images.Digit Prediction and Visualization
Make predictions on test images.
Display predicted vs. actual labels for visual confirmation.Learning Outcome
Gain practical exposure to CNNs, image classification, and TensorFlow/Keras.
Build a strong foundation for more advanced projects in computer vision.