Generative AI and LLMs: Architecture and Data Preparation

IBM via Coursera

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Overview

This IBM short course, a part of Generative AI Engineering Essentials with LLMs Professional Certificate, will teach you the basics of using generative AI and Large Language Models (LLMs). This course is suitable for existing and aspiring data scientists, machine learning engineers, deep-learning engineers, and AI engineers. You will learn about the types of generative AI and its real-world applications. You will gain the knowledge to differentiate between various generative AI architectures and models, such as Recurrent Neural Networks (RNNs),...

Syllabus

Generative AI Architecture

In this module, you will learn about the significance of generative AI models and how they are used across a wide range of fields for generating various types of content. You will learn about the architectures and models commonly used in generative AI and the differences in the training approaches of these models. You will learn how large language models (LLMs) are used to build NLP-based applications. You will build a simple chatbot using the transformers library from Hugging Face.

Data Preparation for LLMs

In this module, you will learn to prepare data for training large language models (LLMs) by implementing tokenization. You will learn about the tokenization methods and the use of tokenizers. You will also learn about the purpose of data loaders and how you can use the DataLoader class in PyTorch. You will implement tokenization using various libraries such as nltk, spaCy, BertTokenizer, and XLNetTokenizer. You will also create a data loader with a collate function that processes batches of text.