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Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [ 2]
v. t. e. Word2vec is a technique in natural language processing (NLP) for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect ...
The word index is a list that maps unique words to specific numerical identifiers, and the tokenized text replaces each word with its corresponding numerical token. These numerical tokens are then used in various deep learning methods. [25] For a language like English, this is fairly
t. e. In natural language processing(NLP), a word embeddingis a representation of a word. The embedding is used in text analysis. Typically, the representation is a real-valuedvector that encodes the meaning of the word in such a way that the words that are closer in the vector space are expected to be similar in meaning.[1] Word embeddings can ...
Google Translate's NMT system uses a large artificial neural network capable of deep learning. [1] [2] [3] By using millions of examples, GNMT improves the quality of translation, [2] using broader context to deduce the most relevant translation. The result is then rearranged and adapted to approach grammatically based human language. [1]
[3] [4] For languages with relatively deep orthographies such as English, French, unvocalised Arabic [citation needed] or Hebrew, new readers have much more difficulty learning to decode words. As a result, children learn to read more slowly. [5] For languages with relatively shallow orthographies, such as Italian and Finnish, new readers have ...
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