**Binary Classification**

- 1 (cat) vs 0 (non cat)
- example Cat image
- if Red, Green, Blue 64 x 64 pixels matrices
- input feature vector : 64 x 64 x 3 dimension = (64 x 64 x 3, 1) matrix

- Notation
- (x,y) : single training example is represented by a pair. x is an x-dimensional feature vector and y is label 0 or 1
- m training examples : {}
- X : D (single training example dimension) by m(# of training) matrix (combined training examples)
- Y : [ ]

**Logistic Regression**

- Given X, want = P(y=1 | x)
- #sigmoid function

**Logistic Regression Cost Function**

- Given {}, want close to
- Loss (error) function
- must not be convex function
- so, use

- Cost function

**Gradient Descent**

- Want to find w,b that minimize
- In case, Repeat { }
- use instead of , if parameters are more than 1, like

**Derivatives**

- slope of the function
- by differential calculus

**Derivatives with a Computation Graph**

- about back propagation
- using chain rule
- da, db, etc below are python variable name

**Logistic Regression Gradient Descent**

**Gradient Descent on m Examples**

*Related*