AI Practitioner: BERT Best Practices & Design Considerations

Artificial Intelligence    |    Expert
  • 17 videos | 57m 55s
  • Includes Assessment
  • Earns a Badge
Rating 3.6 of 12 users Rating 3.6 of 12 users (12)
Bidirectional Encoder Representations from Transformers (BERT), a natural language processing technique, takes the capabilities of language AI systems to great heights. Google's BERT reports state-of-the-art performance on several complex tasks in natural language understanding. In this course, you'll examine the fundamentals of traditional NLP and distinguish them from more advanced techniques, like BERT. You'll identify the terms attention and transformer and how they relate to NLP. You'll then examine a series of real-life applications of BERT, such as in SEO and masking. Next, you'll work with an NLP pipeline utilizing BERT in Python for various tasks, namely, text tokenization and encoding, model definition and training, and data augmentation and prediction. Finally, you'll recognize the benefits of using BERT and TensorFlow together.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Recall traditional natural language processing techniques and approaches
    Describe the limitations of traditional natural language processing techniques and list potential breakthroughs
    Define the terms "attention" and "transformer" as they relate to natural language processing
    Specify the role of natural language processing techniques like bert
    Describe how utilizing bert techniques helps improve search quality
    Outline how bert techniques facilitate context specificity
    List ways of using bert techniques for search engine optimization
    Describe how masking is used in bert
  • Demonstrate how to do data augmentation using masking and bert in python
    Illustrate how to do text tokenization using bert in python
    Show how to do text encoding using bert in python
    Define a bert model in python and create and compile the bert layer using tensorflow
    Train a bert model in python and identify the various hyperparameters for bert
    Demonstrate how to do data prediction using bert in python, load a trained bert model, create the sample data, and predict using the model
    Describe how the use of the tensorflow package can advance bert techniques
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 31s
  • 3m 50s
    Upon completion of this video, you will be able to recall traditional natural language processing techniques and approaches. FREE ACCESS
  • Locked
    3.  Limitations of Traditional NLP
    2m 46s
    After completing this video, you will be able to describe the limitations of traditional natural language processing techniques and list potential breakthroughs. FREE ACCESS
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    4.  Attention and Transformers
    4m 2s
    In this video, you will define the terms "attention" and "transformer" as they relate to natural language processing. FREE ACCESS
  • Locked
    5.  Natural Language Processing and BERT
    4m 18s
    Upon completion of this video, you will be able to specify the role of natural language processing techniques, like BERT. FREE ACCESS
  • Locked
    6.  BERT and Search Quality
    3m
    After completing this video, you will be able to describe how utilizing BERT techniques can help improve search quality. FREE ACCESS
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    7.  BERT and Context Specificity
    3m 12s
    In this video, you will outline how BERT techniques can help with context specificity. FREE ACCESS
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    8.  BERT and Keywords in SEO
    3m 56s
    Upon completion of this video, you will be able to list ways of using BERT techniques for search engine optimization. FREE ACCESS
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    9.  BERT and Masking
    2m 53s
    Upon completion of this video, you will be able to describe how masking is used in BERT. FREE ACCESS
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    10.  Using BERT for Data Augmentation
    4m 6s
    In this video, you will learn how to do data augmentation using masking and BERT in Python. FREE ACCESS
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    11.  Using BERT for Text Tokenization
    3m 16s
    After completing this video, you will be able to illustrate how to do text tokenization using BERT in Python. FREE ACCESS
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    12.  Using BERT for Text Encoding
    3m 32s
    In this video, you will show how to do text encoding using BERT in Python. FREE ACCESS
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    13.  Using BERT for Model Definition
    4m 33s
    Learn how to define a BERT model in Python, create the BERT layer, and compile it using TensorFlow. FREE ACCESS
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    14.  Using BERT for Model Training
    4m 11s
    During this video, you will learn how to train a BERT model in Python and identify the various hyperparameters for BERT. FREE ACCESS
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    15.  Using BERT for Data Prediction
    3m 51s
    In this video, you will learn how to do data prediction using BERT in Python. You will load a trained BERT model, create the sample data, and predict using the model. FREE ACCESS
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    16.  BERT and TensorFlow
    2m 58s
    After completing this video, you will be able to describe how the use of the TensorFlow package can improve BERT techniques. FREE ACCESS
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    17.  Course Summary
    1m 1s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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