Project Title: Deep Learning-Based Handwritten Digit Recognition for MNIST Dataset

1. Project Overview and Objective:

   • Developed a deep learning model using PyTorch to perform handwritten digit recognition on the MNIST dataset.
   • Objective: Achieve high accuracy in classifying handwritten digits (0-9) using a convolutional neural network (CNN) architecture.

2. Key Contributions:

   • Utilized PyTorch, an open-source deep learning framework, to build, train, and evaluate the model.
   • Demonstrated proficiency in data preprocessing, model architecture design, and evaluation.

3. Project Steps and Accomplishments:

   • Data Preparation:

     • Loaded the MNIST dataset using the torchvision library.
     • Transformed images into tensors and normalized pixel values.

   • Model Architecture:

     • Designed a CNN architecture (Convolutional Neural Network) to extract features from images.
     • Utilized convolutional and pooling layers, along with dropout for regularization.
     • Implemented fully connected layers for final digit classification.

   • Training:

     • Divided the dataset into training and validation sets for model evaluation.
     • Trained the model over 20 epochs using the Adam optimizer and cross-entropy loss.
     • Monitored loss and accuracy during training to assess model performance.

   • Validation and Overfitting Check:

     • Plotted training and validation loss curves to visualize model convergence and overfitting.
     • Analyzed accuracy trends to identify possible underfitting or overfitting issues.

   • Evaluation and Reporting:

     • Evaluated the trained model on the test dataset for accuracy assessment and achieved 99.1% accuracy.
     • Generated a comprehensive classification report using scikit-learn to quantify performance across classes.