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.
What you'll learn
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.
Table of contents
- Overview 2m
- Maximum Likelihood Estimation 5m
- The Continuous Bag of Words Neural Network 6m
- The Skip-gram Neural Network 1m
- Useful Python Packages 2m
- Demo: Download Data and Extract Words 5m
- Demo: Build and Prepare Dataset 4m
- Demo: Generate Training Batches 6m
- Demo: Contruct the Neural Network 8m
- Demo: Train the Neural Network 6m
- Noise Contrastive Estimators to Measure Loss 5m
- Demo: Implementing Noise Contrastive Estimation 9m
- Summary 1m
- Naive Bayes Intuition 7m
- Demo: Implementing Naive Bayes as a Baseline 10m
- Drawbacks of Naive Bayes 2m
- Demo: Data Preparation for Classification Using RNNs 5m
- Demo: Build and Run the Neural Network 9m
- Advantages of RNNs for Sentiment Analysis 2m
- Demo: Use Pre-trained GloVe Embeddings for Classification 7m
- Summary and Further Learning 2m