Deep learning model techniques
Andrzej Dudek · 2025
This chapter explains deep learning models based on neural network techniques and their relevance to economic modeling. Starting with the foundations of neural networks, it covers the building blocks of these models: activation functions, loss functions, performance metrics, and optimization algorithms. Different neural network paradigms (architectures) are introduced, such as feedforward networks, convolutional networks, recurrent networks, and advanced architectures like transformers and generative adversarial networks. The goal is to explain how these tools expand the analytical capabilities of traditional quantitative methods.