Ready to explore the exciting world of generative AI and large language models (LLMs)? This IBM course, part of the Generative AI Engineering Essentials with LLMs Professional Certificate, gives you practical skills to harness AI to transform industries.
Generative AI and LLMs: Architecture and Data Preparation
Generative AI and LLMs: Architecture and Data Preparation
This course is part of multiple programs.
Instructors: Joseph Santarcangelo
Access provided by New York State Department of Labor
20,903 already enrolled
Gain insight into a topic and learn the fundamentals.
4.7
(213 reviews)
Intermediate level
Recommended experience
5 hours to complete
Flexible schedule
Learn at your own pace
What you'll learn
Differentiate between generative AI architectures and models, such as RNNs, transformers, VAEs, GANs, and diffusion models
Describe how LLMs, such as GPT, BERT, BART, and T5, are applied in natural language processing tasks
Implement tokenization to preprocess raw text using NLP libraries like NLTK, spaCy, BertTokenizer, and XLNetTokenizer
Create an NLP data loader in PyTorch that handles tokenization, numericalization, and padding for text datasets
Skills you'll gain
Details to know
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Assessments
4 assignments
Taught in English
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There are 2 modules in this course
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.
What's included
5 videos2 readings2 assignments1 app item3 plugins
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.
What's included
2 videos5 readings2 assignments2 app items2 plugins
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4.7
213 reviews
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5.02%
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Showing 3 of 213
TL
5
Reviewed on Mar 3, 2025
this course was very beneficial with detail material and easy to understand
BB
4
Reviewed on Mar 25, 2025
Too fast reading of the slides without much of explanations.
YL
4
Reviewed on May 5, 2025
This is a fairly easy course, focusing on introducing the high-level concepts, without too much hands-on practices
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