Why This Site Exists
Deep learning is often explained in one of two unsatisfying ways.
On one side, there are polished intuitions with weak mathematical substance. On the other, there are formal treatments that are correct but pedagogically inert.
This site exists to close that gap.
The goal is to build a reference that is rigorous enough for serious study, but structured clearly enough to support real understanding. The standard is not merely correctness. The standard is explanatory power.
Important
The central ambition of this site is to maintain a real balance between mathematical rigor and intuition. Intuition without mathematics is unstable. Mathematics without intuition is inert. Good exposition needs both.
What These Notes Try to Do
These notes are written for students, researchers, and ML engineers who want more than slogans, diagrams, or recipe-style tutorials.
The aim is to explain why a method works, what assumptions it relies on, how the equations are organized, and where the clean mathematical story collides with practical implementation.
Note
These notes are not written as a sequence of simplified anecdotes. The preferred path is to use notation, derivations, and structure to build intuition rather than to replace it.
Whenever possible, the exposition follows a simple progression:
- define the problem precisely
- introduce notation explicitly
- derive the base result
- extend it to the vector, tensor, or architectural setting
- connect it to training dynamics, numerical stability, and framework behavior
Principles
Mathematics Is the Explanation
Weak metaphors are not a substitute for understanding. When a concept is fundamentally algebraic, probabilistic, or dynamical, the explanation should reflect that.
Rigor Should Clarify, Not Obscure
Proof details matter when they reveal structure. They matter less when they only increase formality without increasing insight.
Theory and Practice Must Meet
A formula is not complete until it is interpreted in the setting where models are actually trained: finite memory, imperfect precision, batched computation, autodiff systems, and optimization constraints.
Good Notes Respect the Reader
Notation should be introduced before it is used. Assumptions should be stated. Difficult points should be isolated rather than hidden behind compressed exposition.
Warning
These notes assume prior familiarity with linear algebra, calculus, multivariate calculus, and probability theory. They are written to deepen understanding, not to replace the mathematical prerequisites on which the subject rests.
What These Notes Are Not
These notes are not optimized for speed-reading, interview prep, or superficial familiarity.
They are not a collection of motivational analogies, and they do not aim to flatten difficult ideas into slogans. Some topics require patience. The point is not to make every idea look easy. The point is to make it intelligible.
The Intended Use
This site is best used as a long-horizon reference.
You can read it sequentially within a topic, use it to revisit a derivation you have forgotten, or connect ideas across areas that are too often taught in isolation: optimization, information theory, architectures, and generative modeling.
If these notes succeed, they should help the reader move from recognition to command.
Info
The intended reader is not a complete beginner. The best use of this site is to study with pencil and paper, reconstruct derivations, and move repeatedly between equations, implementation details, and conceptual structure.