# 👩‍🏫 Notation

In the context of text processing (e.g: Natural Language Processing NLP)

 Symbol Description ​$X^{}$​ The tth word in the input sequence ​$Y^{}$​ The tth word in the output sequence ​$X^{(i)}$​ The tth word in the ith input sequence ​$Y^{(i)}$​ The tth word in the ith output sequence ​$T^{(i)}_x$​ The length of the ith input sequence ​$T^{(i)}_y$​ The length of the ith output sequence

# 🚀 One Hot Encoding

A way to represent words so we can treat with them easily

## 🔎 Example

Let's say that we have a dictionary that consists of 10 words (🤭) and the words of the dictionary are:

• Car, Pen, Girl, Berry, Apple, Likes, The, And, Boy, Book.

Our $X^{(i)}$ is: The Girl Likes Apple And Berry

So we can represent this sequence like the following 👀

Car   -0)  ⌈ 0 ⌉   ⌈ 0 ⌉   ⌈ 0 ⌉   ⌈ 0 ⌉  ⌈ 0 ⌉   ⌈ 0 ⌉ Pen   -1)  | 0 |  | 0 |  | 0 |  | 0 |  | 0 |  | 0 |Girl  -2)  | 0 |  | 1 |  | 0 |  | 0 |  | 0 |  | 0 |Berry -3)  | 0 |  | 0 |  | 0 |  | 0 |  | 0 |  | 1 |Apple -4)  | 0 |  | 0 |  | 0 |  | 1 |  | 0 |  | 0 |Likes -5)  | 0 |  | 0 |  | 1 |  | 0 |  | 0 |  | 0 |The   -6)  | 1 |  | 0 |  | 0 |  | 0 |  | 0 |  | 0 |And   -7)  | 0 |  | 0 |  | 0 |  | 0 |  | 1 |  | 0 |Boy   -8)  | 0 |  | 0 |  | 0 |  | 0 |  | 0 |  | 0 |Book  -9)  ⌊ 0 ⌋   ⌊ 0 ⌋   ⌊ 0 ⌋   ⌊ 0 ⌋  ⌊ 0 ⌋   ⌊ 0 ⌋

By representing sequences in this way we can feed out data to neural networks ✨