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