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.
Natural Language Processing with Sequence Models is a part of one structured learning path.