Model Regresi Linear
Model: $y = w \cdot x$ (disederhanakan tanpa
intercept)
Loss Function: $L = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$ (Mean Squared Error)
Loss Function: $L = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$ (Mean Squared Error)
Automatic Gradient Descent
Watch as the algorithm automatically adjusts the weight to minimize the loss:
0.10
3.50
3.50
Current Loss (MSE):
0.000
Siap memulai algoritma gradient descent
Data Points & Garis Regresi
Loss Function & Jalur Gradient Descent
Informasi Algoritma
Aturan Update Gradient Descent:
$w_{new} = w_{old} - \alpha \cdot \frac{\partial L}{\partial w}$
Gradient (Derivative of Loss):
$\frac{\partial L}{\partial w} = \frac{2}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i) \cdot x_i$
Iterasi Saat Ini: 0
Gradient Saat Ini: 0.000
$w_{new} = w_{old} - \alpha \cdot \frac{\partial L}{\partial w}$
Gradient (Derivative of Loss):
$\frac{\partial L}{\partial w} = \frac{2}{n} \sum_{i=1}^{n} (\hat{y}_i - y_i) \cdot x_i$
Iterasi Saat Ini: 0
Gradient Saat Ini: 0.000
| # | Nilai X | Nilai Y (True) | Y Prediksi | Loss (Squared Error) |
|---|