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
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describe language models and how to implement natural language processing (NLP) and its componentsgenerate sequences for natural language processing (NLP)describe vanishing gradient problem implementation approaches
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list features and characteristics of gated recurrent units (GRUs)recognize the problems and drawbacks of implementing short-term memory and LSTM as modeling solutionslist characteristics of language modeling, vanishing gradient problems, and LSTM
IN THIS COURSE
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1.Course Overview1m 36sUP NEXT
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2.Language Model and NLP5m 6s
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3.Sequence Generation for NLP2m 36s
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4.Vanishing Gradient Problem2m 14s
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5.Gated Recurrent Unit (GRU)2m 45s
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6.Long Short-Term Memory (LSTM) Network2m 52s
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7.Exercise: Language Modeling1m 43s
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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