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

Concepts4

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
📚TheoryAdvanced

GAN Theory & Training Dynamics

GANs frame learning as a two-player game where a generator tries to fool a discriminator, and the discriminator tries to detect fakes.

#gan#generator#discriminator+12
📚TheoryIntermediate

Spectral Regularization

Spectral regularization controls how much a weight matrix can stretch inputs by constraining its largest singular value (spectral norm).

#spectral regularization
Advanced
Filtering by:
#spectral normalization
#spectral norm
#power iteration
+11
📚TheoryIntermediate

Spectral Normalization

Spectral normalization rescales a weight matrix so its largest singular value (spectral norm) is at most a target value, typically 1.

#spectral normalization#spectral norm#singular value+12
📚TheoryAdvanced

GAN Theory

Generative Adversarial Networks (GANs) set up a two-player game where a generator tries to make fake samples that look real while a discriminator tries to tell real from fake.

#gan minimax#wasserstein gan#js divergence+11