Word2vec easily explained data science

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word2vec: a deep dive with easy explanations and code examples

word2vec is a powerful technique in natural language processing (nlp) for learning **word embeddings**, which are dense, low-dimensional vector representations of words. these embeddings capture semantic relationships between words, meaning words with similar meanings will have vectors that are close to each other in the vector space. this allows us to perform various nlp tasks like:

* **semantic similarity:** finding words that are similar in meaning to a given word.
* **analogy completion:** solving analogies like "king is to queen as man is to woman".
* **machine translation:** learning mappings between word embeddings in different languages.
* **text classification:** using word embeddings as features for classifying documents.

**why word embeddings?**

traditional approaches to representing words, like one-hot encoding, suffer from the curse of dimensionality and fail to capture semantic relationships. one-hot encoding assigns a unique vector of all zeros except for a single '1' to each word. this results in:

* **high dimensionality:** if you have a vocabulary of 10,000 words, each word's vector will be 10,000-dimensional.
* **sparsity:** most of the vector elements are zero, leading to inefficient storage and computation.
* **lack of semantic information:** one-hot vectors treat all words as equally dissimilar, regardless of their meanings.

word embeddings address these issues by representing words as dense, low-dimensional vectors (typically 50-300 dimensions) where the values in the vector are learned from the training data. the learned vectors capture the contextual information of the words.

**two main architectures: cbow and skip-gram**

word2vec comes in two main flavors:

1. **continuous bag-of-words (cbow):** this model predicts the target word given its surrounding context words. it takes the average of the context word vectors as input and tries to predict the ta ...

#Word2Vec #DataScience #coding
Word2Vec
data science
word embeddings
neural networks
natural language processing
semantic similarity
vector representation
training algorithm
skip-gram model
continuous bag of words
context words
feature extraction
language models
dimensionality reduction
unsupervised learning

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