Suppose we have some training set $(x_{(i)}, y_{(i)})$ for $i = 1, \dots, m$. Also suppose we run some type of supervised learning algorithm on the training set. Hypotheses are represented as $h_{\theta}(x_{(i)}) = \theta_0+\theta_{1}x_{(i)1} + \cdots +\theta_{n}x_{(i)n}$. We need to find the parameters $\mathbf{\theta}$ that minimize the "distance" between $y_{(i)}$ and $h_{\theta}(x_{(i)})$. Let $$J(\theta) = \frac{1}{2} \sum_{i=1}^{m} (y_{(i)}-h_{\theta}(x_{(i)})^{2}$$

Then we want to find $\theta$ that minimizes $J(\theta)$. In gradient descent we initialize each parameter and perform the following update: $$\theta_j := \theta_j-\alpha \frac{\partial}{\partial \theta_{j}} J(\theta)$$

>**Question.** What is the key difference between batch gradient descent and stochastic gradient descent? 

Both use the above update rule. But is one better than the other?