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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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N-gram is a continuous sequence of n items from a given sample text. It means that we have a given text and we
have to consider some continuous n items. We can put any number in the position of n. If we take n=1 then it
will be called 1-gram or unigram. If we take n=2 then 2-gram or bigram, similarly n=3 is 3-gram or trigram,
and so on.
Example:
Here take a DNA sequence =AGCTTCAA
Let's see 1-gram or unigram:
A,G,C,T,T,C,A,A
Here we will take or consider one item at a time.
Let's see 2-gram or unigram:
AG,CT,GC,TT,TC,CG,GA,CA Here we will take or consider two items at a time.
Let's see 3-gram or unigram:
AGc,GCT,CTT,TTG,TGA
Here we will take or consider three items at a time.
Let's see another example:
Here we have a sentence: The sun is shining
Let's see 1-gram or unigram:
["The","sun","is","shining"]
Here we will take or consider one item at a time.
Let's see 2-gram or unigram:
["The sun","sun is","is shining"]
Here we will take or consider two items at a time.
Let's see 3-gram or unigram:
["The sun is","sun is shining"]
Here we will take or consider three items at a time.