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Concepts10

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

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🔷All∑Math⚙️Algo🗂️DS📚Theory

Level

AllBeginnerIntermediate
📚TheoryAdvanced

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
📚TheoryAdvanced

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
Advanced
Group:
Representation Learning
#simplex etf
#equiangular tight frame
+12
⚙️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
⚙️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
📚TheoryIntermediate

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
📚TheoryAdvanced

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#hΔh-divergence+12
📚TheoryAdvanced

Disentangled Representations

Disentangled representations aim to encode independent factors of variation (like shape, size, or color) into separate coordinates of a latent vector.

#disentangled representations#independent factors#total correlation+12
📚TheoryIntermediate

Self-Supervised Learning Theory

Self-supervised learning (SSL) teaches models to learn useful representations from unlabeled data by solving proxy tasks created directly from the data.

#self-supervised learning#contrastive learning#infonce+12
📚TheoryIntermediate

Contrastive Learning

Contrastive learning teaches models by pulling together similar examples (positives) and pushing apart dissimilar ones (negatives).

#contrastive learning#infonce#nt-xent+12
📚TheoryIntermediate

Embedding Spaces & Distributed Representations

Embedding spaces map discrete things like words or products to dense vectors so that similar items are close together.

#embeddings#dense vectors#cosine similarity+12