Information Theory
Entropy, cross-entropy, KL divergence, and mutual information
Jan 26, 2025 · 1 min read
Mathematics for AI
Linear algebra, calculus, probability, and statistics
Linear algebra, calculus, probability, and statistics
AI/ML
Mathematics
Second-Order Optimization Methods: Newton, Quasi-Newton, and L-BFGS for Machine Learning
Master second-order optimization methods for machine learning. Learn Newton's method, Quasi-Newton, …
Jan 15, 2024 · 14 min read
AI/ML
Mathematics
Information Theory: Entropy, Cross-Entropy, and KL Divergence
Master information theory for machine learning. Learn entropy, cross-entropy loss, KL divergence, …
Jan 15, 2024 · 16 min read
AI/ML
Mathematics
Covariance, Correlation, and Multivariate Distributions in ML
Master covariance matrices, correlation analysis, and multivariate distributions for machine …
Jan 15, 2024 · 13 min read
AI/ML
Mathematics
Convex Optimization for Machine Learning: From Lagrange to KKT Conditions
Master convex optimization fundamentals for machine learning. Learn convex functions, Lagrange …
Jan 15, 2024 · 14 min read
AI/ML
Mathematics
Statistical Inference for ML: Hypothesis Testing and Confidence Intervals
Master statistical inference for machine learning. Learn hypothesis testing, p-values, confidence …
Jan 15, 2024 · 15 min read
AI/ML
Deep Learning
Learning Rate Schedules: Warmup, Cosine Annealing, and One-Cycle Policy
Master learning rate scheduling for deep learning. Implement warmup, step decay, cosine annealing, …
Jan 15, 2024 · 13 min read
AI/ML
Mathematics
MLE vs MAP: Maximum Likelihood and Bayesian Parameter Estimation
Master MLE and MAP estimation for machine learning. Learn when to use each, mathematical …
Jan 15, 2024 · 13 min read
AI/ML
Deep Learning
Gradient Descent Optimizers: SGD, Adam, RMSprop Complete Comparison
Master gradient descent optimizers with practical examples. Compare SGD, Momentum, Adam, RMSprop, …
Jan 15, 2024 · 15 min read
AI/ML
Mathematics
Bayes' Theorem Explained: From Basics to Bayesian Machine Learning
Master Bayes' theorem for machine learning. Learn prior, posterior, likelihood with intuitive …
Jan 15, 2024 · 15 min read
AI/ML
Deep Learning
Backpropagation Explained: How Neural Networks Actually Learn
Master backpropagation with step-by-step derivations, computational graphs, and practical code …
Jan 15, 2024 · 15 min read
AI/ML
Mathematics
Probability Distributions for Machine Learning: Complete Guide
Master probability distributions essential for ML. Learn Gaussian, Bernoulli, Poisson, and more with …
Jan 15, 2024 · 15 min read
AI/ML
Mathematics
Calculus for Deep Learning: Derivatives, Gradients, and Chain Rule Explained
Master the calculus foundations for deep learning. Learn derivatives, gradients, partial …
Jan 15, 2024 · 15 min read