Generative AI and LLMs: Architecture and Data Preparation

Generative AI and LLMs: Architecture and Data Preparation

Taught in English


Gain insight into a topic and learn the fundamentals

Joseph Santarcangelo
Roodra Pratap Kanwar

Instructors: Joseph Santarcangelo

Intermediate level

Recommended experience

5 hours to complete
3 weeks at 1 hour a week
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 used in language processing.

  • Implement tokenization to preprocess raw textual data using NLP libraries such as NLTK, spaCy, BertTokenizer, and XLNetTokenizer.

  • Create an NLP data loader using PyTorch to perform tokenization, numericalization, and padding of text data.

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May 2024


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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

4 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 videos4 readings2 assignments2 app items2 plugins


Joseph Santarcangelo
28 Courses1,390,023 learners

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