Self-Organizing Maps, Boltzmann Machines, and Autoencoders in Deep Learning Models

This chapter delves into the concepts of self-organizing maps, Boltzmann machines, and autoencoders in deep learning models. It explains the unique characteristics and applications of each model, from self-organizing maps' simplicity to Boltzmann machines' complexity and autoencoders' ability to solve complex problems with stacked structures. The discussion also touches on the creativity involved in data science and the roles of 'artist' and 'engineer' in developing innovative model structures.