In Course 3 of the Natural Language Processing Specialization, you will:
a) Train a neural network with GLoVe word embeddings to perform sentiment analysis of tweets,
b) Generate synthetic Shakespeare text using a Gated Recurrent Unit (GRU) language model,
c) Train a recurrent neural network to perform named entity recognition (NER) using LSTMs with linear layers, and
d) Use so-called ‘Siamese’ LSTM models to compare questions in a corpus and identify those that are worded differently but have the same meaning.Read more.
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The techniques and tools covered in Natural Language Processing with Sequence Models are most similar to the requirements found in Data Scientist job advertisements.
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