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Bridge toward Cramer and signed volumes

Understanding how linear maps move oriented normals prepares determinant-based solution formulas in...

Advanced Mathematics
Linear system as unknown combination of columns

The system Amathbfx=mathbfb asks which linear combination of columns of A produces mathbfb. Write...

Intermediate Mathematics
2×2 picture: area ratios

In mathbbR^2, columns mathbfa_1,mathbfa_2 span a parallelogram. Replacing mathbfa_1 by mathbfb...

Intermediate Mathematics
3×3 parallelepiped story

In mathbbR^3, columns of A span a parallelepiped. Interpret mathbfb inside that solid; ratios of...

Intermediate Mathematics
Algebraic cofactor mirror

Minors and cofactors reorganize the same volume ratios into Laplace expansion form [@2]. The...

Intermediate Mathematics
Failure modes and teaching payoff

Near-zero denominators amplify noise; singular systems need consistency checks, not blind division...

Intermediate Mathematics
Transition to change of basis formulas

Coordinate remapping reinterprets the same geometry in new language. Chapter 13 develops change of...

Intermediate Mathematics
Mean, square-root mnemonic

If two numbers share mean m and product p, they sit symmetrically about m: write them as mpm d for...

Intermediate Mathematics
Neurons: weighted sums, bias, then a nonlinearity

This chapter poses a core design question: given activations in one layer, what knobs let a network...

Beginner Machine learning
Why depth needs nonlinear breaks

Before the algebra, the narrative asks for a hope about hidden layers: maybe a penultimate layer...

Beginner Machine learning
MNIST-style digits as a running example

The running example is handwritten digit recognition on 28times 28 grayscale images, 784 pixels...

Beginner Machine learning
Parameter counting and width vs depth intuition

Every weight and bias is a learnable knob. The demo network connects all 784 inputs to each of 16...

Beginner Machine learning