🎓How I Study AIHISA
📖Read
📄Papers📰Blogs🎬Courses
💡Learn
🛤️Paths📚Topics💡Concepts🎴Shorts
🎯Practice
⏱️Coach🧩Problems🧠Thinking🎯Prompts🧠Review
SearchSettings
How I Study AI - Learn AI Papers & Lectures the Easy Way

Concepts2

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

AllBeginner
📚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

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
Intermediate
Advanced
Filtering by:
#unsupervised learning
Group:
Representation Learning
#independent factors
#total correlation
+12