Attention-based Models and Transformers for Natural Language Processing

NLP    |    Intermediate
  • 15 videos | 2h 20m 16s
  • Includes Assessment
  • Earns a Badge
Rating 4.3 of 6 users Rating 4.3 of 6 users (6)
Attention mechanisms in natural language processing (NLP) allow models to dynamically focus on different parts of the input data, enhancing their ability to understand context and relationships within the text. This significantly improves the performance of tasks such as translation, sentiment analysis, and question-answering by enabling models to process and interpret complex language structures more effectively. Begin this course by setting up language translation models and exploring the foundational concepts of translation models, including the encoder-decoder structure. Then you will investigate the basic translation process by building a transformer model based on recurrent neural networks without attention. Next, you will incorporate an attention layer into the decoder of your language translation model. You will discover how transformers process input sequences in parallel, improving efficiency and training speed through the use of positional and word embeddings. Finally, you will learn about queries, keys, and values within the multi-head attention layer, culminating in training a transformer model for language translation.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Clean and visualize text data
    Preprocess data for language translation
    Set up an encoder-decoder model
    Calculate the loss and accuracy for a translation model
    Train and generate predictions using an encoder-decoder model
    Set up a decoder model with attention
    Generate translations using an attention-based model
  • Provide an overview of transformer models for language processing
    Describe how multi-head attention works
    Calculate query, key, and value for transformer models
    Preprocess data for a transformer model
    Set up the encoder and decoder
    Train a transformer model
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 19s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 10m 48s
    After completing this video, you will be able to clean and visualize text data. FREE ACCESS
  • Locked
    3.  Preparing Data for Language Translation
    14m 2s
    During this video, you will learn how to preprocess data for language translation. FREE ACCESS
  • Locked
    4.  Configuring the Encoder-Decoder Architecture
    9m 33s
    Find out how to set up an encoder-decoder model. FREE ACCESS
  • Locked
    5.  Defining the Loss and Accuracy for the Translation Model
    4m 43s
    In this video, discover how to calculate the loss and accuracy for a translation model. FREE ACCESS
  • Locked
    6.  Training Validation and Prediction Using Encoder and Decoder
    11m 30s
    Learn how to train and generate predictions using an encoder-decoder model. FREE ACCESS
  • Locked
    7.  Setting up the Decoder Architecture with Attention Layer
    13m
    In this video, find out how to set up a decoder model with attention. FREE ACCESS
  • Locked
    8.  Generating Translations Using the Attention Model
    11m 37s
    During this video, discover how to generate translations using an attention-based model. FREE ACCESS
  • Locked
    9.  The Transformer Architecture: Part I
    8m 51s
    Upon completion of this video, you will be able to provide an overview of transformer models for language processing. FREE ACCESS
  • Locked
    10.  The Transformer Architecture: Part II
    11m 34s
    After completing this video, you will be able to describe how multi-head attention works. FREE ACCESS
  • Locked
    11.  Using Query, Key, and Value in the Attention Mechanism
    11m 22s
    In this video, you will learn how to calculate query, key, and value for transformer models. FREE ACCESS
  • Locked
    12.  Structuring Translations for Input to a Transformer Model
    8m 37s
    Find out how to preprocess data for a transformer model. FREE ACCESS
  • Locked
    13.  Setting up the Encoder and Decoder in the Transformer Architecture
    12m 4s
    Discover how to set up the encoder and decoder. FREE ACCESS
  • Locked
    14.  Training the Transformer Model and Using It for Predictions
    7m 32s
    During this video, you will learn how to train a transformer model. FREE ACCESS
  • Locked
    15.  Course Summary
    2m 45s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.

Digital badges are yours to keep, forever.

YOU MIGHT ALSO LIKE

Journey Natural Language Processing and LLMs
Rating 5.0 of 3 users Rating 5.0 of 3 users (3)
Journey Natural Language Processing
Rating 4.7 of 3 users Rating 4.7 of 3 users (3)
Course Advanced NLP: Language Translation Using Transformer Model
Rating 4.3 of 7 users Rating 4.3 of 7 users (7)