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Concepts18

Groups

📐Linear Algebra15📈Calculus & Differentiation10🎯Optimization14🎲Probability Theory12📊Statistics for ML9📡Information Theory10🔺Convex Optimization7🔢Numerical Methods6🕸Graph Theory for Deep Learning6🔵Topology for ML5🌐Differential Geometry6∞Measure Theory & Functional Analysis6🎰Random Matrix Theory5🌊Fourier Analysis & Signal Processing9🎰Sampling & Monte Carlo Methods10🧠Deep Learning Theory12🛡️Regularization Theory11👁️Attention & Transformer Theory10🎨Generative Model Theory11🔮Representation Learning10🎮Reinforcement Learning Mathematics9🔄Variational Methods8📉Loss Functions & Objectives10⏱️Sequence & Temporal Models8💎Geometric Deep Learning8

Category

🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginnerIntermediate
∑MathIntermediate

Rényi Entropy & Divergence

Rényi entropy generalizes Shannon entropy by measuring uncertainty with a tunable emphasis on common versus rare outcomes.

#renyi entropy#renyi divergence#shannon entropy+12
∑MathIntermediate

Hidden Markov Models

A Hidden Markov Model (HMM) describes sequences where you cannot see the true state directly, but you can observe outputs generated by those hidden states.

#hidden markov model
12
Advanced
Filtering by:
#log-sum-exp
#forward algorithm
#viterbi
+12
📚TheoryIntermediate

Knowledge Distillation Loss

Knowledge distillation loss blends standard hard-label cross-entropy with a soft distribution match from a teacher using a temperature parameter.

#knowledge distillation#kd loss#temperature scaling+12
📚TheoryAdvanced

CTC Loss (Connectionist Temporal Classification)

CTC loss trains sequence models when you do not know the alignment between inputs (frames) and outputs (labels).

#ctc loss#connectionist temporal classification#forward backward+12
∑MathIntermediate

Cross-Entropy Loss

Cross-entropy loss measures how well predicted probabilities match the true labels by penalizing confident wrong predictions heavily.

#cross-entropy#binary cross-entropy#softmax+11
⚙️AlgorithmIntermediate

Expectation Maximization (EM)

Expectation Maximization (EM) is an iterative algorithm to estimate parameters when some variables are hidden or unobserved.

#expectation maximization#em algorithm#e-step+12
∑MathAdvanced

Evidence Lower Bound (ELBO)

The Evidence Lower Bound (ELBO) is a tractable lower bound on the log evidence log p(x) used to perform approximate Bayesian inference.

#elbo#variational inference#vae+12
📚TheoryIntermediate

Maximum Likelihood & Generative Models

Maximum Likelihood Estimation (MLE) picks parameters that make the observed data most probable under a chosen probabilistic model.

#maximum likelihood#generative models#naive bayes+12
∑MathIntermediate

Softmax & Temperature Scaling

Softmax turns arbitrary real-valued scores (logits) into probabilities that sum to one.

#softmax#temperature scaling#logits+12
📚TheoryIntermediate

Scaled Dot-Product Attention

Scaled dot-product attention scores how much each value V should contribute to a query by taking dot products with keys K, scaling by \(\sqrt{d_k}\), applying softmax, and forming a weighted sum.

#scaled dot-product attention#softmax#transformer+10
⚙️AlgorithmIntermediate

Importance Sampling

Importance sampling rewrites an expectation under a hard-to-sample distribution p as an expectation under an easier distribution q, multiplied by a weight w = p/q.

#importance sampling#proposal distribution#self-normalized+12
∑MathIntermediate

Numerical Stability

Numerical stability measures how much rounding and tiny input changes can distort an algorithm’s output on real computers using floating-point arithmetic.

#numerical stability#forward error#backward error+12