Category: Blog
-
How to Learn Reinforcement-Learning Algorithms: Bellman Equations, Value Iteration, Q-Learning, Policy Gradients and Actor–Critic
A beginner-to-professional learning manual for reinforcement-learning algorithms: MDPs, Bellman equations, value iteration, TD learning, Q-learning, DQN, policy gradients, actor–critic, evaluation and safety.
-
How to Learn Graph-Isomorphism Algorithms: Invariants, Color Refinement, VF2++, Individualization and Canonical Forms
A beginner-to-professional learning manual for graph-isomorphism algorithms: invariants, color refinement, Weisfeiler–Leman, VF2++, individualization, canonical labeling, symmetry and practical validation.
-
How to Learn Hidden Markov Model Algorithms: Forward Probabilities, Viterbi Decoding, Forward–Backward and Baum–Welch
A beginner-to-professional learning manual for Hidden Markov Model algorithms: forward probabilities, Viterbi decoding, forward–backward smoothing, Baum–Welch training, numerical stability and model validation.
-
How to Learn Numerical Integration Algorithms: Trapezoids, Simpson’s Rule, Gaussian Quadrature and Adaptive Error Control
A beginner-to-professional learning manual for numerical integration algorithms: trapezoidal and Simpson rules, Gaussian quadrature, adaptive error control, singularities, validation and production method choice.
-
How to Learn Krylov-Subspace Algorithms: Conjugate Gradient, GMRES, Residuals and Preconditioning
A beginner-to-professional learning manual for Krylov-subspace algorithms: residuals, Conjugate Gradient, GMRES, preconditioning, convergence diagnostics, sparse computation and production trade-offs.
-
How to Learn Numerical ODE Solver Algorithms: Euler, Runge–Kutta, Adaptive Step Size, Stiffness and BDF
A beginner-to-professional learning manual for numerical ODE solvers: Euler, Runge–Kutta, adaptive error control, dense output, events, stiffness, implicit methods, BDF and professional validation.
