Gen AI Foundational Models for NLP & Language Understanding

IBM via Coursera

Go to Course: https://www.coursera.org/learn/gen-ai-foundational-models-for-nlp-and-language-understanding

Introduction

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.

Syllabus

Fundamentals of Language Understanding

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.

Word2Vec and Sequence-to-Sequence Models

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.