Deep Learning for NLP: Memory-based Networks

Natural Language Processing    |    Intermediate
  • 12 Videos | 1h 27m 5s
  • Includes Assessment
  • Earns a Badge
In the journey to understand deep learning models for natural language processing (NLP), the subsequent iterations are memory-based networks, which are much more capable of handling extended context in languages. While basic neural networks are better than machine learning (ML) models, they still lack in more significant and large language data problems. In this course, you will learn about memory-based networks like gated recurrent unit (GRU) and long short-term memory (LSTM). Explore their architectures, variants, and where they work and fail for NLP. Then, consider their implementations using product classification data and compare different results to understand each architecture's effectiveness. Upon completing this course, you will have learned the basics of memory-based networks and their implementation in TensorFlow to understand the effect of memory and more extended context for NLP datasets.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    outline the importance of memory-based learning and the different networks it supports
    outline gated recurrent unit (GRU) and how it differs from recurrent neural networks (RNNs)
    outline long short-term memory (LSTM) networks and how they differ from RNN
    illustrate how LSTM networks work better and solve the vanishing gradient problem
    illustrate different types of LSTM networks
  • perform data preparation for LSTM and GRU networks
    perform review classification using GRU
    perform review classification using LSTM
    perform review classification using bidirectional long short-term memory (Bi-LSTM)
    compare results of important features across different networks
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 17s
    UP NEXT
  • Playable
    2. 
    Introduction to Memory-based Networks
    4m 28s
  • Locked
    3. 
    Gated Recurrent Unit (GRU) Architecture
    9m 9s
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    4. 
    Long Short-term Memory (LSTM) Architecture
    8m 40s
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    5. 
    Fall of RNN versus Rise of LSTM
    1m 53s
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    6. 
    Variants of LSTM networks
    4m 39s
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    7. 
    Product Review Data Preparation for Modeling
    10m 35s
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    8. 
    Product Review Data Classification Using GRU
    10m 16s
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    9. 
    Product Review Data Classification Using LSTM
    10m 58s
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    10. 
    Product Review Data Classification Using Bi-LSTM
    12m 9s
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    11. 
    Result Comparison between RNN, GRU, and LSTM
    11m 35s
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    12. 
    Course Summary
    1m 26s

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