HomeCoursesEssentials of Large Language Models: A Beginner’s Journey

Beginner

2h

Essentials of Large Language Models: A Beginner’s Journey
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In this LLM course, learn the LLM fundamentals, fine-tuning, and evaluation, and explore its architecture and evolution. Discover GPT-2, compare models, and uncover use cases.
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Course Overview

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-tun...Show More
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 in fine-tuning LLMs to specific datasets and evaluating their performance
An understanding of language models, large language models, and their key differences

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TAKEAWAY SKILLS

Generative AI

Large Language Models (LLMs)

Course Content

1.

Course Overview

1 Lessons

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

2.

Getting Started with LLMs

7 Lessons

Look at advanced LLM fundamentals, components, types, capabilities, examples, and limitations.

3.

Fine-Tuning LLMs

6 Lessons

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

4.

Wrap Up

1 Lessons

Find out about applying language models creatively and responsibly to impact AI.
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