Deep Learning for NLP: Transfer Learning

Natural Language Processing    |    Intermediate
  • 16 Videos | 2h 10m 20s
  • Includes Assessment
  • Earns a Badge
The essential aspect of human intelligence is our learning processes, constantly augmented with the transfer of concepts and fundamentals. For example, as a child, we learn the basic alphabet, grammar, and words, and through the transfer of these fundamentals, we can then read books and communicate with people. This is what transfer learning helps us achieve in deep learning as well. This course will help you learn the fundamentals of transfer learning for NLP, its various challenges, and use cases. Explore various transfer learning models such as ELMo and ULMFiT. Upon completing this course, you will understand the transfer learning methodology of solving NLP problems and be able to experiment with various models in TensorFlow.

WHAT YOU WILL LEARN

  • discover the key concepts covered in this course
    define transfer learning and illustrate how it helps to get better results
    outline advantages and challenges of transfer learning in real world problem solving
    illustrate the use of language modeling in Transfer learning
    outline key concepts related to FastText and Word2Vec
    outline key concepts related to ELMo
    outline key concepts realted to ULMFiT
    build a ELMo embedding layer for product reviews data classification
  • create an ELMo model for product reviews data classification
    perform review classification using ELMo and FastText
    reshape data to adjust to ELMo embedding layer requirements
    build a simple language model using ULMFiT on the product reviews data
    implement and fine tune the LM model using ULMFiT
    perform review classification using ULMFIT and FastText
    illustrate model comparison
    summarize the key concepts covered in this course

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    1m 40s
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    2. 
    Introduction to Transfer Learning
    10m 12s
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    3. 
    Advantages and Challenges of Transfer Learning
    3m 53s
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    4. 
    Role of Language Modeling in Transfer Learning
    6m
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    5. 
    Introduction to Basic Transfer Learning Models
    5m 58s
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    6. 
    Intermediate Transfer Learning Models
    3m 44s
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    7. 
    Advance Transfer Learning Models
    3m 18s
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    8. 
    Building ELMo Embedding Layer for Reviews
    16m 32s
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    9. 
    Creating ELMo an Model for Product Reviews
    9m 4s
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    10. 
    Classifying Product Reviews Using ELMo
    12m 20s
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    11. 
    Reshaping Data for the ELMo Embedding Layer
    11m 22s
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    12. 
    Building a Language Model Using ULMFiT
    12m 56s
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    13. 
    Implementing the Language Model Using ULMFiT
    14m 55s
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    14. 
    Classifying Product Reviews Using ULMFIT & FastText
    15m 1s
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    15. 
    Performing Result Comparison
    2m 9s
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    16. 
    Course Summary
    1m 16s
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