Hi,

I’m probably missing something here but, as can be seen below, the Python function to compute the gradient has two loops, whereas the equation for the gradient with respect to wj only has one summation symbol.

```
def compute_gradient(X, y, w, b):
"""
Computes the gradient for linear regression
Args:
X (ndarray (m,n)): Data, m examples with n features
y (ndarray (m,)) : target values
w (ndarray (n,)) : model parameters
b (scalar) : model parameter
Returns:
dj_dw (ndarray (n,)): The gradient of the cost w.r.t. the parameters w.
dj_db (scalar): The gradient of the cost w.r.t. the parameter b.
"""
m,n = X.shape #(number of examples, number of features)
dj_dw = np.zeros((n,))
dj_db = 0.
for i in range(m):
err = (np.dot(X[i], w) + b) - y[i]
for j in range(n):
dj_dw[j] = dj_dw[j] + err * X[i, j]
dj_db = dj_db + err
dj_dw = dj_dw / m
dj_db = dj_db / m
return dj_db, dj_dw
```

The way I’m thinking about this, the equation should have a second summation symbol, like this:

Am I wrong about this? Can someone please explain this to me. Thanks!