shallow neural network
2018, Sep 22
Explanation for vectorized implementation
In this lecture, we study vectorized implementation. It’s simple. with vetorization, we don’t need to use for-loop for all data.
we just define Data X, Weight W, (+ bias) and do the math with Numpy.
To understand the below, recap the notation.
\(a^{[1](i)}\) : activation result of 1st layer and i-th element(node).
