Summary
In this chapter, we explored how to effectively use AI assistants like ChatGPT to learn and experiment with convolutional neural network (CNN) models. The strategies provided a clear step-by-step approach to experimenting with different techniques for building and training CNN models using the CIFAR-10 dataset.
Each step was accompanied by detailed instructions, code generation, and user validation, ensuring a structured learning experience. We started by building a baseline CNN model, where we learned the essential preprocessing steps, including normalizing pixel values and resizing images. It guided you through generating beginner-friendly code that is compatible with Jupyter notebooks, ensuring that even those new to the field could easily grasp the fundamentals of CNN construction.
As we progressed, our AI assistant became an integral part of the learning process, helping us delve into more complex areas such as adding layers, implementing dropout and batch normalization...
