Sentiment Analysis with Recurrent Neural Networks in TensorFlow

Recurrent neural networks (RNNs) are ideal for considering sequences of data. In this course, you'll explore how word embeddings are used for sentiment analysis using neural networks.
Course info
Rating
(37)
Level
Intermediate
Updated
Dec 20, 2017
Duration
2h 54m
Table of contents
Description
Course info
Rating
(37)
Level
Intermediate
Updated
Dec 20, 2017
Duration
2h 54m
Description

Sentiment analysis and natural language processing are common problems to solve using machine learning techniques. Having accurate and good answers to questions without trudging through reviews requires the application of deep learning techniques such as neural networks. In this course, Sentiment Analysis with Recurrent Neural Networks in TensorFlow, you'll learn how to utilize recurrent neural networks (RNNs) to classify movie reviews based on sentiment. First, you'll discover how to generate word embeddings using the skip-gram method in the word2vec model, and see how this neural network can be optimized by using a special loss function, the noise contrastive estimator. Next, you'll delve into understanding RNNs and how to implement an RNN to classify movie reviews, and compare and contrast the neural network implementation with a standard machine learning model, the Naive Bayes algorithm. Finally, you'll learn how to implement the same RNN but with pre-built word embeddings. By the end of this course, you'll be able to understand and implement word embedding algorithms to generate numeric representations of text, and know how to build a basic classification model with RNNs using these word embeddings.

About the author
About the author

A problem solver at heart, Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework.

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