Deep Neural Network for Image Classification with MNIST

This notebook demonstrates building deep neural network classifiers for handwritten digit classification on the MNIST dataset. Various model architectures and techniques are implemented using TensorFlow and Keras.

The models implemented include:

• Basic multi-layer perceptron (MLP) with fully connected layers
• Convolutional neural networks (CNN) with convolutional and pooling layers
• Dropout regularization and learning rate decay for improved generalizability
• Data augmentation to expand the training dataset

These models achieve over 97-99% accuracy on the MNIST test dataset, competitive with state-of-the-art results. Through accessible code examples and detailed comments, readers can understand the fundamentals of deep learning and apply these learnings to tackle their machine learning problems.

Image of CNN model:

The goal is to provide a practical introductory notebook covering neural network best practices using TensorFlow and Keras, applicable for those wanting hands-on experience in deep learning for computer vision tasks. Readers should finish with an understanding of how to build, train, and evaluate complex models using the latest frameworks.

Let me know if you have any other questions!