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How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts532

Groups

๐Ÿ“Linear Algebra15๐Ÿ“ˆCalculus & Differentiation10๐ŸŽฏOptimization14๐ŸŽฒProbability Theory12๐Ÿ“ŠStatistics for ML9๐Ÿ“กInformation Theory10๐Ÿ”บConvex Optimization7๐Ÿ”ขNumerical Methods6๐Ÿ•ธGraph Theory for Deep Learning6๐Ÿ”ตTopology for ML5๐ŸŒDifferential Geometry6โˆžMeasure Theory & Functional Analysis6๐ŸŽฐRandom Matrix Theory5๐ŸŒŠFourier Analysis & Signal Processing9๐ŸŽฐSampling & Monte Carlo Methods10๐Ÿง Deep Learning Theory12๐Ÿ›ก๏ธRegularization Theory11๐Ÿ‘๏ธAttention & Transformer Theory10๐ŸŽจGenerative Model Theory11๐Ÿ”ฎRepresentation Learning10๐ŸŽฎReinforcement Learning Mathematics9๐Ÿ”„Variational Methods8๐Ÿ“‰Loss Functions & Objectives10โฑ๏ธSequence & Temporal Models8๐Ÿ’ŽGeometric Deep Learning8

Category

๐Ÿ”ทAllโˆ‘Mathโš™๏ธAlgo๐Ÿ—‚๏ธDS๐Ÿ“šTheory

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AllBeginnerIntermediateAdvanced
๐Ÿ“šTheoryIntermediate

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
๐Ÿ“šTheoryIntermediate

Exploration-Exploitation Tradeoff

The explorationโ€“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
34567
#exploration exploitation
#ucb1
+12
๐Ÿ“šTheoryIntermediate

Value Function Approximation

Value function approximation replaces a huge table of values with a small set of parameters that can generalize across similar states.

#reinforcement learning#value function approximation#linear function approximator+12
โš™๏ธAlgorithmIntermediate

PPO & Trust Region Methods

Proximal Policy Optimization (PPO) stabilizes policy gradient learning by preventing each update from moving the policy too far from the previous one.

#ppo#trust region#trpo+11
โš™๏ธAlgorithmIntermediate

Temporal Difference Learning

Temporal Difference (TD) Learning updates value estimates by bootstrapping from the next state's current estimate, enabling fast, online learning.

#temporal difference learning#td(0)#sarsa+12
โˆ‘MathIntermediate

Markov Decision Processes (MDP)

A Markov Decision Process (MDP) models decision-making in situations where outcomes are partly random and partly under the control of a decision maker.

#markov decision process#value iteration#policy iteration+12
๐Ÿ“šTheoryAdvanced

Manifold Learning

Manifold learning assumes high-dimensional data actually lies near a much lower-dimensional, smoothly curved surface embedded in a higher-dimensional space.

#manifold learning#isomap#locally linear embedding+12
๐Ÿ“šTheoryAdvanced

Neural Collapse

Neural Collapse describes what happens at the end of training: the penultimate-layer features of each class concentrate tightly around a class mean.

#neural collapse#simplex etf#equiangular tight frame+12
โš™๏ธAlgorithmIntermediate

t-SNE & UMAP

t-SNE and UMAP are nonlinear dimensionality-reduction methods that preserve local neighborhoods to make high-dimensional data visible in 2D or 3D.

#t-sne#umap#dimensionality reduction+12
โš™๏ธAlgorithmIntermediate

Principal Component Analysis (PCA)

Principal Component Analysis (PCA) finds new orthogonal axes (principal components) that capture the maximum variance in your data.

#principal component analysis#pca c++#eigendecomposition+11
๐Ÿ“šTheoryIntermediate

Metric Learning

Metric learning is about automatically learning a distance function so that similar items are close and dissimilar items are far in a feature space.

#metric learning#mahalanobis distance#contrastive loss+12
๐Ÿ“šTheoryAdvanced

Transfer Learning Theory

Transfer learning theory studies when and why a model trained on a source distribution will work on a different target distribution.

#transfer learning#domain adaptation#hฮ”h-divergence+12