About the Archive

Gradient and Reason is an inquiry into the nature of modern machine learning systems, viewed through the lens of classical philosophy. We are building systems of unprecedented accuracy and utility, yet they are increasingly opaque, underdetermined, and detached from mechanistic understanding.

This archive is structured as a collection of empirical essays. We believe that philosophical questions should not remain purely abstract, nor should engineering practice remain blindly pragmatic. Each essay integrates rigorous, self-contained Python / PyTorch code alongside philosophical interrogation.

I am Ashmit Mandal, a researcher and developer working at the fragile intersection of Machine Learning, Data Science, and Epistemology. Currently studying computer science at KIIT, my work is driven by a quiet skepticism of the black boxes we construct: I investigate neural network interpretability, model compression trade-offs, and causal inference.

This blog serves as a digital archive of diagnostics, computational autopsies, and philosophical notes—an effort to reconcile the mathematical elegance of high-dimensional manifolds with the limits of human understanding. When not exploring causal models or latent space geometry, I spend my time examining the epistemological foundations of machine learning systems and the limits of data analytics.

You can reach me via email or follow the progress on GitHub.

Essays on this site are registered in a central repository of ideas. If you find these discussions helpful for your academic or technical work, you can cite them using standard BibTeX references available on each essay page.