DeepLearning.AI
Natural Language Processing in TensorFlow
DeepLearning.AI

Natural Language Processing in TensorFlow

Laurence Moroney

Instructor: Laurence Moroney

Sponsored by Coursera for Reliance Family

146,651 already enrolled

Gain insight into a topic and learn the fundamentals.
4.6

(6,491 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 23 hours
Learn at your own pace
94%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.6

(6,491 reviews)

Intermediate level

Recommended experience

Flexible schedule
Approx. 23 hours
Learn at your own pace
94%
Most learners liked this course

What you'll learn

  • Build natural language processing systems using TensorFlow

  • Process text, including tokenization and representing sentences as vectors

  • Apply RNNs, GRUs, and LSTMs in TensorFlow

  • Train LSTMs on existing text to create original poetry and more

Details to know

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Assessments

4 assignments

Taught in English

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There are 4 modules in this course

The first step in understanding sentiment in text, and in particular when training a neural network to do so is the tokenization of that text. This is the process of converting the text into numeric values, with a number representing a word or a character. This week you'll learn about the Tokenizer and pad_sequences APIs in TensorFlow and how they can be used to prepare and encode text and sentences to get them ready for training neural networks!

What's included

13 videos7 readings1 assignment1 programming assignment3 ungraded labs

Last week you saw how to use the Tokenizer to prepare your text to be used by a neural network by converting words into numeric tokens, and sequencing sentences from these tokens. This week you'll learn about Embeddings, where these tokens are mapped as vectors in a high dimension space. With Embeddings and labelled examples, these vectors can then be tuned so that words with similar meaning will have a similar direction in the vector space. This will begin the process of training a neural network to understand sentiment in text -- and you'll begin by looking at movie reviews, training a neural network on texts that are labelled 'positive' or 'negative' and determining which words in a sentence drive those meanings.

What's included

12 videos4 readings1 assignment1 programming assignment3 ungraded labs

In the last couple of weeks you looked first at Tokenizing words to get numeric values from them, and then using Embeddings to group words of similar meaning depending on how they were labelled. This gave you a good, but rough, sentiment analysis -- words such as 'fun' and 'entertaining' might show up in a positive movie review, and 'boring' and 'dull' might show up in a negative one. But sentiment can also be determined by the sequence in which words appear. For example, you could have 'not fun', which of course is the opposite of 'fun'. This week you'll start digging into a variety of model formats that are used in training models to understand context in sequence!

What's included

10 videos4 readings1 assignment1 programming assignment6 ungraded labs

Taking everything that you've learned in training a neural network based on NLP, we thought it might be a bit of fun to turn the tables away from classification and use your knowledge for prediction. Given a body of words, you could conceivably predict the word most likely to follow a given word or phrase, and once you've done that, to do it again, and again. With that in mind, this week you'll build a poetry generator. It's trained with the lyrics from traditional Irish songs, and can be used to produce beautiful-sounding verse of it's own!

What's included

14 videos5 readings1 assignment1 programming assignment3 ungraded labs

Instructor

Instructor ratings
4.8 (855 ratings)
Laurence Moroney
DeepLearning.AI
19 Courses531,926 learners

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DeepLearning.AI

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4.6

6,491 reviews

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