Difference between Softmax and multiclass logistic regression? I know that logistic regression is for binary outputs and Softmax is for multiple classes. Would it be fair to say that Softmax regression is the same thing as multiclass Logistic regression?
 A: Softmax regression is a generalization of logistic regression.
Remember in logistic regression labels and model parameters were:
$$
y^{(i)} \in \{0,1\},\space
\theta =
\begin{bmatrix}
\theta_1 \\
\theta_2 \\
\vdots \\
\theta_n
\end{bmatrix}
$$
Whereas in softmax regression labels and model parameters are:
$$
y^{(i)} \in \{1, 2, \ldots, K\},\space
\theta =
\begin{bmatrix}
\theta_1^1 & \theta_1^2 & \theta_1^k \\
\theta_2^1 & \theta_2^2 & \theta_2^k \\
\vdots & \vdots & \vdots\\
\theta_n^1 & \theta_n^2 & \theta_n^k \\
\end{bmatrix}
$$
In that sence it is easy to see that logistic regression can be expressed as softmax with two classes. Of course, cost functions and hypothesis are a bit different.

Would it be fair to say that Softmax regression is the same thing as multiclass Logistic regression?

It's more about the naming convention: when talking about a two-class problem, we usually call it logistic regression.
Reference: http://ufldl.stanford.edu/tutorial/supervised/SoftmaxRegression/
