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Concepts8

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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

Multi-Task Loss Balancing

Multi-task loss balancing aims to automatically set each task’s weight so that no single loss dominates training.

#multi-task learning#uncertainty weighting#homoscedastic uncertainty+12
∑MathIntermediate

Cross-Entropy Loss

Cross-entropy loss measures how well predicted probabilities match the true labels by penalizing confident wrong predictions heavily.

#cross-entropy
Advanced
Filtering by:
#maximum likelihood
#binary cross-entropy
#softmax
+11
⚙️AlgorithmIntermediate

Expectation Maximization (EM)

Expectation Maximization (EM) is an iterative algorithm to estimate parameters when some variables are hidden or unobserved.

#expectation maximization#em algorithm#e-step+12
📚TheoryIntermediate

Autoregressive Models

Autoregressive (AR) models represent a joint distribution by multiplying conditional probabilities in a fixed order, using the chain rule of probability.

#autoregressive#ar model#n-gram+11
📚TheoryIntermediate

Maximum Likelihood & Generative Models

Maximum Likelihood Estimation (MLE) picks parameters that make the observed data most probable under a chosen probabilistic model.

#maximum likelihood#generative models#naive bayes+12
📚TheoryIntermediate

Cross-Entropy

Cross-entropy measures how well a proposed distribution Q predicts outcomes actually generated by a true distribution P.

#cross-entropy#entropy#kl divergence+12
∑MathIntermediate

Maximum Likelihood Estimation (MLE)

Maximum Likelihood Estimation (MLE) chooses parameters that make the observed data most probable under a chosen model.

#maximum likelihood#log-likelihood#bernoulli mle+12
∑MathIntermediate

Exponential Family Distributions

Exponential family distributions express many common probability models in a single template p(x|η) = h(x) exp(η^T T(x) − A(η)).

#exponential family#natural parameter#sufficient statistics+12