IBM
Gen AI Foundational Models for NLP & Language Understanding
IBM

Gen AI Foundational Models for NLP & Language Understanding

This course is part of multiple programs.

Joseph Santarcangelo
Fateme Akbari

Instructors: Joseph Santarcangelo

Sponsored by MAHE Manipal

3,392 already enrolled

Gain insight into a topic and learn the fundamentals.
4.4

(38 reviews)

Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.4

(38 reviews)

Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

What you'll learn

  • Explain how to use one-hot encoding, bag-of-words, embedding, and embedding bags to convert words to features.

  • Build and use word2vec models for contextual embedding.

  • Build and train a simple language model with a neural network.

  • Utilize N-gram and sequence-to-sequence models for document classification, text analysis, and sequence transformation.

Details to know

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Assessments

5 assignments

Taught in English

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There are 2 modules in this course

In this module, you will learn about one-hot encoding, bag-of-words, embeddings, and embedding bags. You will also gain knowledge of neural networks and their hyperparameters, cross-entropy loss, and optimization. You will then delve into the concept of language modeling with n-grams. The module also includes hands-on labs on document classification with PyTorch and building a simple language model with a neural network.

What's included

7 videos4 readings3 assignments2 app items1 plugin

In this module, you will learn about the word2vec embedding model and its types. You will also be introduced to sequence-to-sequence models and how they employ Recurrent neural networks (RNNs) to process variable-length input sequences and generate variable-length output sequences. You will gain insights about encoder-decoder RNN models, their architecture, and how to build them using PyTorch. The module will give you knowledge about evaluating the quality of text using perplexity, precision, and recall in text generation. In hands-on labs, you will integrate pre-trained embedding models for text analysis or classification and develop a sequence-to-sequence model for sequence transformation tasks.

What's included

6 videos5 readings2 assignments2 app items3 plugins

Instructors

Instructor ratings
3.7 (7 ratings)
Joseph Santarcangelo
IBM
33 Courses1,706,314 learners

Offered by

IBM

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