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Concepts356

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📐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

All Beginner Intermediate

📚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

📚TheoryIntermediate

Perceptual Loss & Feature Matching

Perceptual loss compares images in a deep network's feature space rather than raw pixels, which aligns better with human judgment of similarity.

#perceptual loss

#feature matching

#gan

+12

📚TheoryIntermediate

Triplet Loss & Contrastive Loss

Triplet loss and contrastive loss are metric-learning objectives that teach a model to map similar items close together and dissimilar items far apart in an embedding space.

#triplet loss#contrastive loss#metric learning+12

📚TheoryIntermediate

Focal Loss

Focal Loss reshapes cross-entropy so that hard, misclassified examples get more focus while easy, well-classified ones are down-weighted.

#focal loss#class imbalance#cross-entropy+11

∑MathIntermediate

Huber Loss & Smooth L1

Huber loss behaves like mean squared error (quadratic) for small residuals and like mean absolute error (linear) for large residuals, making it both stable and robust.

#huber loss#smooth l1#robust regression+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

📚TheoryIntermediate

Mean Field Variational Family

Mean field variational family assumes the joint posterior over latent variables factorizes into independent pieces q(z) = ∏ q_i(z_i).

#mean field#variational inference#elbo+11

📚TheoryIntermediate

Variational Inference

Variational Inference (VI) turns Bayesian inference into an optimization problem by choosing a simple family q(z) to approximate an intractable posterior p(z|x).

#variational inference#elbo#kl divergence+12

∑MathIntermediate

Discount Factor & Return

The discounted return G_t sums all future rewards but down-weights distant rewards by powers of a discount factor γ.

#discount factor#discounted return#reinforcement learning+12

📚TheoryIntermediate

RLHF Mathematics

RLHF turns human preferences between two model outputs into training signals using a probabilistic model of choice.

#rlhf#bradley-terry#pairwise comparisons+11

📚TheoryIntermediate

Exploration-Exploitation Tradeoff

The exploration–exploitation tradeoff is the tension between trying new actions to learn (exploration) and using the best-known action to earn rewards now (exploitation).

#multi-armed bandit#exploration exploitation#ucb1+12