About — The Lattice of Deep Learning
Deep learning is often taught as a collection of architectures, tricks, and code recipes.
This publication starts from a different premise:
Deep learning is a system of ideas.
The Lattice of Deep Learning is an attempt to make those ideas explicit.
Inspired by the notion of building a latticework of mental models—an approach famously articulated by Charlie Munger—this publication develops a structured way of thinking about deep learning, rather than merely using it.
Why a Lattice?
Most confusion in deep learning does not come from missing equations or APIs.
It comes from missing conceptual scaffolding.
When ideas like optimization, representation, attention, generalization, and scale are learned in isolation, they remain fragile. A lattice connects them.
Each essay in this publication introduces a mental model and connects it to others already introduced. Over time, these models form a coherent framework for reasoning about neural networks.
Not a linear curriculum.
A lattice.
What Are Mental Models (In This Context)?
In this publication, mental models act as analogies and metaphors that allow us to:
Remember complex ideas more easily
Reason about systems without immediately resorting to formal equations
Perform first-principles thinking when theory or implementation details are unavailable
A good mental model compresses complexity.
These metaphors are not replacements for formal theory.
They are entry points.
Mathematics, proofs, and formal frameworks can always be invoked later—but only after intuition has been built. Mental models provide a way to think clearly before thinking formally.
Here’s a quote from Jeremy Howard that captures this philosophy:
“Whoever you listen to — whether it be Feynman or whoever — you always hear from the great scientists how they build deeper intuition by building mental models, which they get over time by interacting with the things that they’re learning about.”
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