Large Language Models (LLMs) Concepts
Discover the full potential of LLMs with our conceptual course covering LLM applications, training methodologies, ethical considerations, and latest research.
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Course Description
Discover Large Language Models
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.This conceptual course will dig into LLMs and how they revolutionize businesses and everyday life with real-world examples, from finance to content creation.
Unlock the Secrets of LLMs and Training Methodologies
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.Explore LLMs Concerns and Considerations
You’ll also tackle the critical ethical and environmental considerations in building and training LLMs, such as training data and privacy concerns.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.
By the end of this course, you'll have a comprehensive understanding of LLMs, their capabilities, applications, and intriguing challenges.
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Get your team access to the full DataCamp platform, including all the features.In the following Tracks
- 1
Introduction to Large Language Models (LLM)
FreeThe 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 landscape50 xpDefinition of an LLM50 xpLLMs in the AI landscape100 xpAI vs. LLM applications100 xpReal-world applications50 xpBusiness applications50 xpMultimodal applications100 xpAutomate data-driven tasks50 xpChallenges of language modeling50 xpWhat can a language model do?50 xpSingle vs. multi-task learning100 xp - 2
Building Blocks of LLMs
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 LLMs50 xpProblem solving with LLMs50 xpTraditional models vs. LLMs100 xpGeneralized overview of NLP50 xpData preparation50 xpText preprocessing and representation100 xpWord embeddings over bag-of-words50 xpFine-tuning50 xpChallenges in building LLMs50 xpAdapt a pre-trained model50 xpPre-trained or fine-tuned?100 xpLearning techniques50 xpFine-tune a model50 xpN-shot learning100 xp - 3
Training Methodology and Techniques
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 LLMs50 xpMasked language50 xpPredict the next word50 xpBuilding from scratch100 xpIntroducing the transformer50 xpRelationships between distant words50 xpTransformer components100 xpAttention mechanisms50 xpFocus of multi-head attention50 xpSelf vs. multi-head attention100 xpAdvanced fine-tuning50 xpEnd-to-end training100 xpTraining, tuning & feedback50 xpBuilding an LLM50 xp - 4
Concerns and Considerations
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 considerations50 xpIs your model fair?50 xpUn-biased and relevant100 xpCustomer service of a bank50 xpEthical and environmental concerns50 xpResponsible use50 xpEthics and environment100 xpWhere are LLMs heading?50 xpCreativity vs. efficiency100 xpAnalyzing literary works100 xpTime to wrap-up50 xp
Training 2 or more people?
Get your team access to the full DataCamp platform, including all the features.In the following Tracks
collaborators
prerequisites
Understanding Machine Learning
Vidhi ChughAI Strategist and Ethicist
Vidhi is an AI Strategist and Ethicist working at the intersection of data science, product, and engineering to build scalable machine learning systems. Listed as one of the "Top 200 Business and Technology Innovators" in the world, Vidhi is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.
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