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Introduction to LLMs in Python

Intermediate3 hr

Learn the nuts and bolts of LLMs and the revolutionary transformer architecture they are based on!

Python3 hr11 videos34 Exercises2,700 XP35,098Statement of accomplishment

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Course Description

Uncover What's Behind the Large Language Models Hype



Large Language Models (LLMs) have become pivotal tools driving some of the most stunning advancements and applications in today's AI landscape. This hands-on course will equip you with the practical knowledge and skills needed to understand, build, and harness the power of LLMs for solving complex language tasks such as translation, language generation, and more.

Discover LLM Architecture and Leverage Pre-Trained Models



Through interactive coding exercises, you'll discover different transformer architectures and how to identify them. You'll explore leveraging pre-trained language models and datasets from Hugging Face for fine-tuning and evaluating your model using advanced metrics that fit LLMs. Finally, you'll find out more about ethical and bias concerns relevant to LLMs and ways to identify these. By the end of this course, you will be able to build LLMs, fine-tune, and evaluate them using specialized metrics while understanding the key challenges and ethical considerations of enabling real-world LLM applications.

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What you'll learn

  • Define the end-to-end workflow for fine-tuning a pre-trained LLM with Hugging Face
  • Differentiate practical techniques to mitigate bias, hallucination, and other ethical risks when deploying LLMs
  • Distinguish between encoder-only, decoder-only, and encoder-decoder transformer architectures
  • Evaluate model performance by selecting and interpreting appropriate metrics such as accuracy, BLEU, ROUGE, perplexity, and toxicity
  • Identify the primary language tasks large language models can perform and the Hugging Face tools used to run them

Prerequisites

Curriculum

Course outline

1

Getting Started with Large Language Models (LLMs)

Introduction to LLMs in Python

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