The discussion centers on finding books that provide a mathematically rigorous approach to data science, particularly in the context of neural networks and optimization theorems. Recommendations include "Algorithms for Optimization" by Kochenderfer, which features practical examples in Julia, and "The Hundred-Page Machine Learning Book" by Burkov, available as a try-and-buy online option. Additionally, "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Geron is noted for its mathematical discussions, though it may not meet the desired level of rigor. The conversation acknowledges that many rigorous insights are currently found in academic papers rather than textbooks. Several relevant research papers on function approximation, gradient descent effectiveness, and error estimation are also shared, indicating ongoing advancements in the field.