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Introduction

Setup

ANN

Working process ANN

Propagation

Bias parameter

Activation function

Loss function

Overfitting and Underfitting

Optimization function

Chain rule

Minima

Gradient problem

Weight initialization

Dropout

ANN Regression Exercise

ANN Classification Exercise

Hyper parameter tuning

CNN

CNN basics

Convolution

Padding

Pooling

Data argumentation

Flattening

Create Custom Dataset

Binary Classification Exercise

Multiclass Classification Exercise

Transfer learning

Transfer model Basic template

RNN

How RNN works

LSTM

Bidirectional RNN

Sequence to sequence

Attention model

Transformer model

Bag of words

Tokenization & Stop words

Stemming & Lemmatization

TF-IDF

N-Gram

Word embedding

Normalization

Pos tagging

Parser

semantic analysis

Regular expression

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What is normalization?

Suppose we have four words: Loved, Lovely, Loves, and Love. For these four words the root word is love. It means that all these words come from love. Now if we want to normalize these four words into root from then we will have only one-word love. So it means we will remove four words and we will have only one word.

These types of words like loved, lovely, loves and love are called inflected words because these words are inflected from the word love.

Suppose we have a document of 10000 words. So there will be a lot of inflected words. Now if we normalize words means remove inflected words and only have the root word then we will be able to minimize our vector in a very less number of columns.

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