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Concepts5

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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

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📚TheoryIntermediate

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
📚TheoryIntermediate

Grokking & Delayed Generalization

Grokking is when a model suddenly starts to generalize well long after it has already memorized the training set.

#grokking
Advanced
Filtering by:
#stochastic gradient descent
#delayed generalization
#weight decay
+12
📚TheoryIntermediate

Empirical Risk Minimization

Empirical Risk Minimization (ERM) chooses a model that minimizes the average loss on the training data.

#empirical risk minimization#expected risk#loss function+12
📚TheoryIntermediate

Randomized Algorithm Theory

Randomized algorithms use random bits to make choices that simplify design, avoid worst cases, and often speed up computation.

#randomized algorithms#las vegas#monte carlo+12
📚TheoryIntermediate

Gradient Descent Convergence Theory

Gradient descent updates parameters by stepping opposite the gradient: x_{t+1} = x_t - \eta \nabla f(x_t).

#gradient descent#convergence rate#l-smooth+12