Fundamentals of Sequence Model: Language Model & Modeling Algorithms

Neural Networks    |    Intermediate
  • 7 videos | 18m 51s
  • Includes Assessment
  • Earns a Badge
In this 7-video course, learners can explore the concepts of language modeling, natural language processing (NLP), and sequence generation for NLP. Prominent machine learning modeling algorithms such as vanishing gradient problem, gated recurrent units (GRUs), and long short-term memory (LSTM) network are also covered. Key concepts studied in this course include language models, one of the most important parts of NLP. and how to implement NLP along with its essential components; learning the process and approach of generating sequence for NLP; and vanishing gradient problem implementation approaches to overcome the problem of taking longer times to achieve convergence. Then, learn about features and characteristics of GRUs used to resolve issues with vanishing gradient problems, and learn the problems and drawbacks of implementing short-term memory and LSTM as modeling solutions. In the concluding exercise, learners will review the essential components and prominent applications of language modeling and specify some of the solutions for vanishing gradient problems.

WHAT YOU WILL LEARN

  • Describe language models and how to implement natural language processing (nlp) and its components
    Generate sequences for natural language processing (nlp)
    Describe vanishing gradient problem implementation approaches
  • List features and characteristics of gated recurrent units (grus)
    Recognize the problems and drawbacks of implementing short-term memory and lstm as modeling solutions
    List characteristics of language modeling, vanishing gradient problems, and lstm

IN THIS COURSE

  • 1m 36s
  • 5m 6s
    Upon completion of this video, you will be able to describe language models and how to implement natural language processing (NLP) and its components. FREE ACCESS
  • Locked
    3.  Sequence Generation for NLP
    2m 36s
    In this video, find out how to generate sequences for natural language processing. FREE ACCESS
  • Locked
    4.  Vanishing Gradient Problem
    2m 14s
    After completing this video, you will be able to describe implementation approaches to the vanishing gradient problem. FREE ACCESS
  • Locked
    5.  Gated Recurrent Unit (GRU)
    2m 45s
    After completing this video, you will be able to list features and characteristics of gated recurrent units (GRUs). FREE ACCESS
  • Locked
    6.  Long Short-Term Memory (LSTM) Network
    2m 52s
    Upon completion of this video, you will be able to recognize the problems and drawbacks of implementing short-term memory and long short-term memory as modeling solutions. FREE ACCESS
  • Locked
    7.  Exercise: Language Modeling
    1m 43s
    Upon completion of this video, you will be able to list characteristics of language modeling, the vanishing gradient problem, and LSTM. FREE ACCESS

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