---
title: "NLP with Deep Learning[WIP]"
description: "Some definitions I run into while working on some NLP work"
date: 2019-06-17
canonical: "https://www.omc345.com/nlp-notes/"
archived: true
---

# Definitions

## SVM (Support Vector Machine)

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.

### Kernel

### Regularization

The Regularization parameter (often termed as C parameter in python’s sklearn library) tells the SVM optimization how much you want to avoid misclassifying each training example.

[Resource](https://medium.com/machine-learning-101/chapter-2-svm-support-vector-machine-theory-f0812effc72)

## Conditional Random Fields as Recurrent Neural Networks (Semantic Segmentation)

[Resource](https://towardsdatascience.com/review-crf-rnn-conditional-random-fields-as-recurrent-neural-networks-semantic-segmentation-a11eb6e40c8c)

### Word Embedding

### Word2Vec

It is magic

### Precision and Recall

[schema](https://upload.wikimedia.org/wikipedia/commons/thumb/2/26/Precisionrecall.svg/1280px-Precisionrecall.svg.png)

### F1 Score

https://en.wikipedia.org/wiki/F1_score

# Vocabulary

## Syntax

The way in which linguistic elements (such as words) are put together to form constituents (such as phrases or clauses)

## Semantic

:of or relating to meaning in language