Breaking the problem down into features
Given the nature of the Fashion-MNIST dataset, which comprises grayscale images of fashion items categorized into different classes, we will start by building a baseline MLP model. This will involve the following high-level steps:
- Building the baseline model: Users will understand the process of constructing a simple MLP model for image classification using ChatGPT. We will guide users through loading the Fashion-MNIST dataset, preprocessing the image data, splitting it into training and testing sets, defining the model architecture, training the model, making predictions, and evaluating its performance.
- Adding layers to the model: Once the baseline model is established, users will learn how to experiment with adding additional layers to the MLP architecture. We will explore how increasing the depth or width of the model impacts its performance and capacity to capture complex patterns in the image data.
- Experimenting with...
