Word2Vec, GLOVE, FastText and Baseline Word Embeddings step

Word2Vec, GLOVE, FastText and Baseline Word Embeddings step

In our previous discussion we had understand the basics of tokenizers step by step. If you had not gone through my previous post i highly recommend just have a look at that post because to understand Embeddings first, we need to understand tokenizers and this post is the continuation of the previous post. I am providing the link below of my post on Tokenizers. I had explained the concepts step by step with a simple example Understanding N

In our previous discussion we had understand the basics of tokenizers step by step. If you had not gone through my previous post i highly recommend just have a look at that post because to understand Embeddings first, we need to understand tokenizers and this post is the continuation of the previous post. I am providing the link below of my post on Tokenizers. I had explained the concepts step by step with a simple example

Understanding NLP Keras Tokenizer Class Arguments with example

As we all know preparation of Input is very important step in complete deep learning pipeline for both image and text…

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There are many more ways like countvectorizer and TF-IDF. But in both, the context of the words are not maintained that results in very low accuracy and again based on different scenarios we need to select. Countvectorizer and TF-IDF is out of scope from this discussion. Coming to embeddings, first we try to understand what the word embedding really means. As we know there are more than *171,476 *of words are there in english language and each word have their different meanings. If we want to represent 171,476 or even more words in the dimensions based on the meaning each of words, then it will result in more than 3–4 lakhs dimension because we have discussed few time ago that each and every words have different meanings and one thing to note there there is a high chance that meaning of word also change based on the context. To understand better about contexual based meaning we will look into below example

Ex- Sentence 1: An apple a day keeps doctor away. Sentence 2: The stock price of Apple is falling down due to COVID-19 pandemic. I

word2vec gloves deep-learning fasttext data-preprocessing

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