Summary

We explored the application of TAG, PIC, and LIFE prompting strategies in crafting regression models, employing both ChatGPT and ChatGPT Plus for rapid analysis and predictive tasks. This approach is particularly valuable in the early stages of machine learning development, offering immediate insights and the flexibility to experiment with different models or algorithms without the burden of managing execution environments or programming instances. Additionally, we learned how to effectively utilize single prompts for generating comprehensive code. While it’s possible to craft prompts for discrete tasks or steps, many of these require only succinct lines of code and were not the focus here. Providing feedback is instrumental in this process, and validating the output is crucial to ensure the code’s functionality.

In the next chapter, we will learn how to use ChatGPT to generate the code for the multilayer perceptron (MLP) model with the help of the Fashion...