Lecture 02. Linear Regression - clairedavid/ml_in_hep GitHub Wiki

    \theta'_0 &= \theta_0-\alpha \frac{\partial}{\partial \theta_0} J\left(\theta\right) \\[2ex]
    \theta'_1 &= \theta_1-\alpha \frac{\partial}{\partial \theta_1} J\left(\theta\right) \\
    \cdots\\
    \theta'_j &= \theta_j-\alpha \frac{\partial}{\partial \theta_j} J\left(\theta\right) \\
    \cdots\\
    \theta'_n &= \theta_n-\alpha \frac{\partial}{\partial \theta_n} J\left(\theta\right) \\
    \end{align*}```



Not

{figure} ../images/lec02_2_normalizedContour.png
---
  name: normalizedContour
  width: 100%
---
 . The 3D rendering the figure above. The $b$ stands for _bias_, a term similar to the offset $\theta_0$ that you will see in the literature (especially with neural networks).  
 <sub>Source: [deeplearning.ai](https://www.deeplearning.ai) | Andrew Ng</sub>