Foundations, done properly

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Curated questions for machine learning fundamentals.

1002 questions · 61 lessons · 13 categories

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Logistic Regression: Deriving Gradients using Matrix Calculus
Information Theory

Logistic Regression: Deriving Gradients using Matrix Calculus

Derivation of $\frac{\partial L}{\partial M}$ for Cross Entropy Loss $L(y) = - \sum_{i=1}^C g\ log(y)$ with ground truth $g$, predicted $y$

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The Core of Backprop 3: Deriving $\frac{\partial L}{\partial M}$ using Matrix Calculus
ML Foundations

The Core of Backprop 3: Deriving $\frac{\partial L}{\partial M}$ using Matrix Calculus

Very elegant & short derivation of: $\frac{\partial L}{\partial M}​$ for loss $L$ to update $M$ in Linear Layers

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Sample vs Population Covariance
Statistics

Sample vs Population Covariance

Why does **Sample Covariance** have: $\frac{1}{n-1} \sum_{i=1}^n (x_i - \bar{x})^2$ but **Population Covariance** $\frac{1}{n} \sum_{i=1}^n (x_i - \bar{x})^2$

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Redis Clusters
Databases

Redis Clusters

What if we need more than one Redis Instance?

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Fourier Series as Basis Functions
Math

Fourier Series as Basis Functions

How the Fourier Series act as Basis Functions in Function Space $L^2[-\pi,\pi]$

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Sinusoidal Positional Encodings
Transformers

Sinusoidal Positional Encodings

Overview of Sinusoidal Positional Encodings from *Attention is All You Need* & *NeRF*

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