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In this LLM course, learn the fundamentals, fine-tuning, and evaluation, and explore its architecture and evolution. Discover GPT-2, compare models, and uncover use cases.

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Overview

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In this course, you will acquire a working knowledge of the capabilities and types of LLMs, along with their importance and limitations in various applications. You will gain valuable hands-on experience by fine-tuning LLMs to specific datasets and evaluating their performance. You will start with an introduction to large language models, looking at components, capabilities, and their types. Next, you will be introduced to GPT-2 as an example of a large language model. Then, you will learn how to fine-tune a selected LLM to a specific dataset, starting from model selection, data preparation, model training, and performance evaluation. You will also compare the performance of two different LLMs. By the end of this course, you will have gained practical experience in fine-tuning LLMs to specific datasets, building a comprehensive skill set for effectively leveraging these generative AI models in diverse language-related applications.

In this course, you will acquire a working knowledge of the capabilities and types of LLMs, along with their importance and limi...Show More

WHAT YOU'LL LEARN

An understanding of language models, large language models, and their key differences

Familiarity with the components of LLMs and their basic architecture

Working knowledge of the types of LLMs, along with their importance and limitations

An understanding of GPT-2 as a large language model

Hands-on experience fine-tuning LLMs to specific datasets and evaluating their performance

An understanding of language models, large language models, and their key differences

TAKEAWAY SKILLS

Generative AI

Large Language Models (LLMs)

Content

15 Lessons3 Quizzes

Course Overview

1 Lessons

Get familiar with large language models, their applications, and ethical considerations in AI.

Introduction to the Course

Getting Started with LLMs

7 Lessons

Learn about advanced LLM fundamentals, components, types, capabilities, examples, and limitations.

Large Language Models

The Transformer Architecture

Evolution of LLMs

Capabilities of LLMs

Example LLM: GPT-2

Exploring Use Cases of LLMs

Importance and Limitations

Fine-Tuning LLMs

6 Lessons

Examine fine-tuning LLMs, model selection, data preparation, training, evaluation, and performance comparison.

Introduction to Fine-Tuning

Challenge: Compare the Performance of Two Different LLMs

Wrap Up

1 Lessons

Learn about applying language models creatively and responsibly to impact AI.

Conclusion

Exploring OpenAI API

Project

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

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Frequently Asked Questions

What is the basic of LLM?

The basics of LLMs include understanding neural networks, transformers, large datasets, and the processes of pre-training and fine-tuning to enable the model to generate human-like text.

Is GPT an LLM?

Yes, GPT (Generative Pre-trained Transformer) is a type of LLM developed by OpenAI, known for its ability to generate coherent and contextually relevant text based on the input it receives.

What is the difference between LLM and AI?

AI (Artificial Intelligence) is a broad field encompassing the creation of intelligent systems, while LLM (Large Language Model) is a specific type of AI focused on understanding and generating human language; LLMs are a subset or tool within the broader field of AI.

What does LLM include?

LLMs include neural networks with millions or billions of parameters, trained on vast datasets of text, and include components for tokenization, embedding, attention mechanisms, and output generation to process and generate human-like language.

Essentials of Large Language Models: A Beginner’s Journey Save

Essentials of Large Language Models: A Beginner’s Journey
Save