The point is, deriving the best PARAMETERs.
Lists of what I organized & recorded :
- Parametric Model & Non-parametric Model
- Model Parameters & Hyper Parameters
- Naive Bayes
- precision and recall & ROC curve
- How to draw ROC Curve... and What is AUC?
...
- What is Gradient?
- Basic of Gradient Descent(LR)
- Generalization of Gradient Descent(LR)
- Analytical solution of Gradient Descent(LR)
- Advanced Gradient Descent (1)
- Advanced Gradient Descent (2)
- Advanced Gradient Descent (3)
- K-fold Cross Validation
- Regularization of Linear Regression
...
- Maximum Likelihood Estimation & Logistic Regression (1)
- Maximum Likelihood Estimation & Logistic Regression (2)
- Gradient Descent in Classification
- Mathematical flow of K-Classification
- K-means Clustering
- Cluster Analysis
- Regression VS Classification
...
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