Using Recurrent Networks For Natural Language Processing

NLP    |    Intermediate
  • 8 videos | 1h 14m 53s
  • Includes Assessment
  • Earns a Badge
Recurrent neural networks (RNNs) are a class of neural networks designed to efficiently process sequential data. Unlike traditional feedforward neural networks, RNNs possess internal memory, which enables them to learn patterns and dependencies in sequential data, making them well-suited for a wide range of applications, including natural language processing. In this course, you will explore the mechanics of RNNs and their capacity for processing sequential data. Next, you will perform sentiment analysis with RNNs, generating and visualizing word embeddings through the TensorBoard embedding projector plug-in. You will construct an RNN, employing these word embeddings for sentiment analysis and evaluating the RNN's efficacy on a set of test data. Then, you will investigate advanced RNN applications, focusing on long short-term memory (LSTM) and bidirectional LSTM models. Finally, you will discover how LSTM models enhance the processing of long text sequences and you will build and train a bidirectional LSTM model to process data in both directions and capture a more comprehensive understanding of the text.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Provide an overview of how to use rnns for processing text data
    Visualize word embeddings with the projector plug-in
    Create word embeddings for model training
  • Train a simple rnn memory cell
    Train rnns with long short-term memory (lstm) and bidirectional lstm
    Perform hyperparameter tuning using the keras tuner
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 17s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 9m 45s
    Upon completion of this video, you will be able to provide an overview of how to use RNNs for processing text data. FREE ACCESS
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    3.  Visualizing Word Embeddings Using the Embedding Projector Plug-in
    10m 8s
    Discover how to visualize word embeddings with the projector plug-in. FREE ACCESS
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    4.  Setting up Word Vector Representations for Training
    11m 44s
    In this video, find out how to create word embeddings for model training. FREE ACCESS
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    5.  Training an RNN for Sentiment Analysis
    11m 28s
    During this video, you will learn how to train a simple RNN memory cell. FREE ACCESS
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    6.  Training an RNN with LSTM and Bidirectional LSTM Layers
    12m 58s
    Find out how to train RNNs with long short-term memory (LSTM) and bidirectional LSTM. FREE ACCESS
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    7.  Performing Hyperparameter Tuning
    13m 48s
    In this video, discover how to perform hyperparameter tuning using the Keras Tuner. FREE ACCESS
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    8.  Course Summary
    2m 45s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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