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This course is part of Natural Language Processing Specialization
Instructors: Younes Bensouda Mourri
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(1,155 reviews)
Recommended experience
Intermediate level
We recommend the first two courses of the Natural Language Processing Specialization
(1,155 reviews)
Recommended experience
Intermediate level
We recommend the first two courses of the Natural Language Processing Specialization
Use recurrent neural networks, LSTMs, GRUs & Siamese networks in TensorFlow for sentiment analysis, text generation & named entity recognition.
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In Course 3 of the Natural Language Processing Specialization, you will:
a) Train a neural network with 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. By the end of this Specialization, you will have designed NLP applications that perform question-answering and sentiment analysis, created tools to translate languages and summarize text! This Specialization is designed and taught by two experts in NLP, machine learning, and deep learning. Younes Bensouda Mourri is an Instructor of AI at Stanford University who also helped build the Deep Learning Specialization. Łukasz Kaiser is a Staff Research Scientist at Google Brain and the co-author of Tensorflow, the Tensor2Tensor and Trax libraries, and the Transformer paper.
Learn about the limitations of traditional language models and see how RNNs and GRUs use sequential data for text prediction. Then build your own next-word generator using a simple RNN on Shakespeare text data!
15 videos15 readings1 assignment2 programming assignments1 app item4 ungraded labs
Learn about how long short-term memory units (LSTMs) solve the vanishing gradient problem, and how Named Entity Recognition systems quickly extract important information from text. Then build your own Named Entity Recognition system using an LSTM and data from Kaggle!
8 videos9 readings1 assignment1 programming assignment1 ungraded lab
Learn about Siamese networks, a special type of neural network made of two identical networks that are eventually merged together, then build your own Siamese network that identifies question duplicates in a dataset from Quora.
10 videos10 readings1 assignment1 programming assignment3 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
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Reviewed on Sep 18, 2020
The course is great and presented excellently with neat visualizations. Introduction to Trax is great and got a chance to learn new framework.
Reviewed on Jan 25, 2021
Concise, to the point, and very insightful/educational. Take it in conjunction with the general Deep Learning Specialization, you'll not regret it.
Reviewed on Sep 20, 2020
Absolutely satisfied with the tons of things I learnt. Professor Jounes and his team did a great work. Looking forward to enrolling to next course.
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