The AI landscape is evolving rapidly, and Large Language Models (LLMs) are at the forefront of this evolution. This chapter examines how LLMs are advancing the development of human-like artificial intelligence and transforming industries through their numerous applications. You will explore the challenges and complexity associated with language modeling.

The rise of LLMs in the AI landscape

Definition of an LLM

LLMs in the AI landscape

AI vs. LLM applications

Real-world applications

Business applications

Multimodal applications

Automate data-driven tasks

Challenges of language modeling

What can a language model do?

Single vs. multi-task learning

Introduction to Large Language Models (LLM)

This chapter emphasizes the novelty of LLMs and their emergent capabilities while outlining various NLP techniques for data preparation. You will learn the challenges of training LLMs and how fine-tuning can effectively address them. You will also understand how N-shot learning techniques enable efficient adaptation of pre-trained models when faced with limited labeled data.

Novelty of LLMs

Problem solving with LLMs

Traditional models vs. LLMs

Generalized overview of NLP

Data preparation

Text preprocessing and representation

Word embeddings over bag-of-words

Fine-tuning

Challenges in building LLMs

Adapt a pre-trained model

Pre-trained or fine-tuned?

Learning techniques

Fine-tune a model

N-shot learning

Building Blocks of LLMs

In this chapter, you will learn about the fundamental building blocks of training an LLM, such as pre-training techniques. You'll also gain an intuitive understanding of complex concepts like transformer architecture, including the attention mechanism. The chapter discusses an advanced fine-tuning technique and summarizes the training process to complete an LLM.

Building blocks to train LLMs

Masked language

Predict the next word

Building from scratch

Introducing the transformer

Relationships between distant words

Transformer components

Attention mechanisms

Focus of multi-head attention

Self vs. multi-head attention

Advanced fine-tuning

End-to-end training

Training, tuning & feedback

Building an LLM

Training Methodology and Techniques

In this chapter, we delve into the key considerations when training LLMs, such as large data availability, data quality, accurate labeling, and the implications of biased data. You will also examine various LLM risks like data privacy, ethical concerns, and environmental impact. Lastly, the chapter concludes by discussing emerging research areas and the evolving landscape of LLMs.

Data concerns and considerations

Is your model fair?

Options

Data Bias

Data Quality and Labeling

Un-biased and relevant

Customer service of a bank

Ethical and environmental concerns

Responsible use

Ethical Concerns

Environmental Impacts

Ethics and environment

Where are LLMs heading?

Creativity

Efficiency

Creativity vs. efficiency

Relevant

Not relevant

Analyzing literary works

Time to wrap-up

Concerns and Considerations

Course Glossary

In this course, you’ll journey through the world of Large Language Models (LLMs) and discover how they are reshaping the AI landscape. You’ll see how they revolutionize businesses and everyday life with real-world examples and learn about their building block. As you progress, you’ll gain insights into the training methodologies too. Finally, you’ll tackle the critical ethical and environmental considerations for LLMs and see what the future of LLMs could look like. By the end of this course, you'll have a comprehensive understanding of LLMs, their capabilities, applications, and challenges.

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.

The course glossary can be found on the right in the resources section.

To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

<h2>Discover Large Language Models</h2>
In this course, you’ll journey through the world of Large Language Models (LLMs) and discover how they are reshaping the AI landscape. You’ll explore the factors fueling the LLM boom, such as the deep learning revolution, data availability, and computing power.
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This conceptual course will dig into LLMs and how they revolutionize businesses and everyday life with real-world examples, from finance to content creation.
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<h2>Unlock the Secrets of LLMs and Training Methodologies</h2>
You’ll learn about the building blocks of LLMs, including natural language processing techniques, fine-tuning strategies, and learning techniques like zero-shot, few-shot, and multi-shot learning. As you progress, you’ll gain insights into the state-of-the-art training methodologies that drive LLMs, including next-word prediction, masked language modeling, and attention mechanisms.
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<h2>Explore LLMs Concerns and Considerations</h2>
You’ll also tackle the critical ethical and environmental considerations in building and training LLMs, such as training data and privacy concerns.
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As you wrap up the course, you’ll discover how to stay ahead of the curve as you delve into the latest research in the LLM field. You’ll explore future developments focusing on model explainability, unsupervised bias handling, computational efficiency, and enhanced creativity.
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By the end of this course, you'll have a comprehensive understanding of LLMs, their capabilities, applications, and intriguing challenges.

Understanding Machine Learning

Discover the full potential of LLMs with our conceptual course covering LLM applications, training methodologies, ethical considerations, and latest research.

Master Large Language Models (LLMs) Concepts

Large Language Models (LLMs) Concepts

AI-basisprincipes

AI strategist and ethicist

Journey so far

Model explainability

Unsupervised bias handling

Enhanced creativity

Let's practice!

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Concerns and Considerations - Where are LLMs heading?

Where are LLMs heading?

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