# 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).