• Fundamentals of Numerical Optimization (NPTEL)
    • 14 - Duality and Its Application on Constrained Optimization
    • 13 - Constrained Optimization - KKT Condition 2
    • 12 - Constrained Optimization - KKT Condition 1
    • 11 - Conjugate Gradient Method
    • 10 - Quasi Newton Method
    • 09 - Classical Newton Method
    • 08 - Steepest Descent Algorithm
    • 07 - Line Search Technique
    • 06 - Multi-Dimensional Unconstrained Optimization
    • 05 - Convex Function
    • 04 - Convex Set
    • 03 - 1-Dimensional Unconstrained Optimization
    • 02 - Mathematical Background
    • 01 - Introduction
  • Numerical Optimization
    • Eigendecomposition and Singular Value Decomposition
    • Mathmatical Background in Detail
    • About Gradient Norm
    • Gradient for Neural Network
    • The Importance of Curvature (a.k.a Hessian) in Numerical Optimization
    • ADMM (Alternating Direction Method of Multipliers)
    • Convexity of Logistic Regression Loss Function
  • Machine Learning
    • Relationship Between Logistic Loss and Cross Entropy Loss
    • MCMC and Gibbs Sampling
    • Note of The Element of Statistical Learning
    • Support Vector Machine
    • Expectation Maximization
    • Various Entropy
    • PLSA and Matrix Factorization
    • Principal Component Analysis
  • Algorithm
    • Dynamic Programming (NPTEL)
  • Data Structure
    • Creating Suffix Array Based on Prefix Doubling and Counting Sort
  • Programming
    • Common Git Use Cases
    • Hadoop Secondary Sort
  • Arch Linux (Arch is the BEST !)
    • Copy Photo from Iphone
    • Install Arch Linux on MacBook Air
    • Arch Linux PPTP Vpn Connection