Project Title: Handwritten Digit Recognition

Objective: To create a deep learning model using a dataset of handwritten digits to accurately recognize and classify the digits from $0$ to $9.$

Guidelines:

$$ Dataset Selection:

o Download the MNIST dataset containing images of handwritten digits $(0-9).$ The dataset is widely available and easy to work with.

$$ Data Preprocessing:

o Normalize the pixel values $(0$ to $255)$ to a range of $0$ to $1.$

o Split the dataset into training and testing sets $($e$.$g$.,80\%$ for training and $20\%$ for testing$).$

$$ CNN Model Development:

o Implement a simple Convolutional Neural Network $($CNN$)$ using TensorFlow or Keras.

o Design a basic architecture consisting of convolutional layers, pooling layers, and fully connected layers.

$$ Training and Evaluation:

o Train the CNN model using the training dataset.

o Evaluate the model's accuracy using the testing dataset.

$$ Hyperparameter Tuning:

o Experiment with different hyperparameters $($e$.$g$.,$ number of layers, kernel sizes, learning rates$)$ to improve the model's performance.

Deliverables:

$$ Code Implementation:

o Python code demonstrating data preprocessing, model creation, training, and evaluation. Keep the code simple and well$-$commented.

$$ Model Performance:

o Report the accuracy achieved by the model on the test set.

$$ Conclusion:

o Summarize the results, any challenges faced, and potential improvements.