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Vectorization refers to a process of converting an algorithm from operating on a single value at a time to operating at one time on a set of values. Vector operations are supported by modern CPUs where a single instruction is applied to multiple data.

In other words, it can be explained as the process of revising loop-based and scalar-oriented code for using MATLAB matrix and vector operations. Students assigned with projects about the subject matter can take Vectorization Assignment Help from the expert professionals of BookMyEssay. But before going into the depth of vectorization and its significance it is necessary to understand the term vectorization thoroughly.

What does the term Vectorization mean?

In simple words, vectorization can be termed as jargon for converting input data from its raw format that is text, into real number vectors which are the ML model supported format.

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Converting of input data is done through a classic approach that has been used since computers were built and it has wonderfully worked in various domains. Now, this approach is used in NLP (Natural Language Processing).

The definition can be understood more easily with Vectorization homework help service.

Techniques of Vectorization:

Some of the techniques of vectorization are written down briefly:

Bag of Words: This is one of the simplest techniques. It includes three operations:

• Tokenization: At first the input text is tokenized. Here, representation of a sentence is done as a list of its essential words and it is done for all input sentences.
• Vocabulary creation: Among the tokenized words, unique words are selected to create a vocabulary and all the unique words are sorted by alphabetical order.
• In this final step, an inadequate matrix is created for teach row is considered as the input, with the frequency of words that were selected for vocabulary. In this matrix, each row is considered as a sentence vector whose length is equal to the vocabulary size.

TF – IDF: TF – IDF is the abbreviated form of Term Frequency – Inverse Document Frequency. It is a numerical statistic that tries to reflect the importance of the word to a document. It is another frequency-based method and it is not as simple as Bag of Words and it has two parts TF and IDF.

Word2Vec: This approach was launched in the year 2013 by Google Researchers and the NLP industry was taken by storm. Simple Neural Network Power is used to generate word embeddings.

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GloVe: it stands for Global Vectors that are used for the representation of words. It was developed at Stanford. Similar to the working of Word2Vec, the intuition of GloVe is to create contextual word embeddings.

Fast Text: it was introduced in the year 2016, by Facebook. It is somehow similar to Word2Vec. It has a unique method of working which is lacking by both Word2Vec and GloVe. The vocabulary is limited for Word2Vec and GloVe even though the models have been trained on billions of words. Fast Text improved over the other methods due to its strong capability of generalization to unique and unknown words, which is missing in all the other methods of vectorization.

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