• Developed a model to recognize handwritten digits from the MNIST dataset.
• Used a convolutional neural network (CNN) architecture to train the model.
• Preprocessed the dataset by normalizing the images and resizing them to a standard size.
• Split the dataset into training and validation sets and used data augmentation techniques to improve the model's generalization capabilities.
• Implemented early stopping and model checkpointing to prevent overfitting and save the best-performing model.
• Achieved an accuracy of over 99% on the test set, demonstrating the effectiveness of the model in recognizing handwritten digits.
• Showcased expertise in deep learning and computer vision through the project.
• Learned valuable skills in machine learning and excited to continue exploring the possibilities in this field.