Category Deep Learning Chapters 8 Difficulty advanced Estimated Time 200 min

Large Language Models (LLMs)

Master Large Language Models from pre-training to fine-tuning. 8 comprehensive chapters covering BERT, GPT, transfer learning, fine-tuning strategies, prompt engineering, and practical applications.

Course Overview

What You Will Build Toward

  • Navigate the Large Language Models (LLMs) learning path across 8 chapters.
  • Choose the right chapter based on your current goal and prerequisites.
  • Move from overview material into the canonical chapter experience.

Chapter Path

Start With Any Chapter

Chapter 1 Introduction to Large Language Models Open chapter Chapter 2 Pre-training Strategies Open chapter Chapter 3 BERT Architecture Open chapter Chapter 4 GPT Architecture Open chapter Chapter 5 Fine-tuning Strategies Open chapter Chapter 6 LoRA & Parameter-Efficient Fine-tuning Open chapter Chapter 7 Prompt Engineering Open chapter Chapter 8 LLM Applications & Best Practices Open chapter

Before You Start

Recommended Background

  • Working knowledge of the course category.
  • Willingness to work through examples and short checks.

Start Chapter 1

Large Language Models (LLMs)

Master Large Language Models from pre-training to fine-tuning. 8 comprehensive chapters covering BERT, GPT, transfer learning, fine-tuning strategies, prompt engineering, and practical applications with detailed explanations, formulas, and code examples.

Chapter 1: Introduction to Large Language Models

The Era of Pre-trained Models

  • What are LLMs? Scale and capabilities
  • Pre-training vs fine-tuning paradigm
  • Transfer learning in NLP
  • Evolution: Word2Vec → BERT → GPT → GPT-3
  • LLM architecture families
Foundation LLMs Pre-training

Chapter 2: Pre-training Strategies

Learning from Unlabeled Data

  • Masked Language Modeling (MLM)
  • Causal Language Modeling (CLM)
  • Next Sentence Prediction (NSP)
  • Pre-training objectives and loss functions
  • Data preparation and tokenization
Pre-training MLM CLM

Chapter 3: BERT (Bidirectional Encoder)

Understanding Encoder-Only Models

  • BERT architecture and components
  • Masked Language Modeling explained
  • BERT variants (RoBERTa, ALBERT, DistilBERT)
  • BERT for classification tasks
  • BERT implementation and usage
BERT Encoder Bidirectional

Chapter 4: GPT (Generative Pre-trained Transformer)

Understanding Decoder-Only Models

  • GPT architecture and autoregressive generation
  • Causal language modeling
  • GPT-2, GPT-3, GPT-4 evolution
  • Text generation mechanics
  • GPT implementation and usage
GPT Decoder Generation

Chapter 5: Fine-tuning LLMs

Adapting Pre-trained Models

  • Why fine-tune? When to fine-tune?
  • Full fine-tuning vs parameter-efficient methods
  • LoRA (Low-Rank Adaptation)
  • Adapter layers and P-tuning
  • Fine-tuning implementation examples
Fine-tuning LoRA Adaptation

Chapter 6: Prompt Engineering

Getting the Most from LLMs

  • What is prompt engineering?
  • Zero-shot, few-shot, and chain-of-thought
  • Prompt templates and patterns
  • In-context learning
  • Advanced prompting techniques
Prompts Few-shot CoT

Chapter 7: LLM Applications & Use Cases

Practical Implementations

  • Text classification with BERT
  • Question answering systems
  • Text generation with GPT
  • Named Entity Recognition (NER)
  • Sentiment analysis and more
Applications Use Cases Practical

Chapter 8: LLM Evaluation & Metrics

Measuring Model Performance

  • Perplexity and language modeling metrics
  • BLEU, ROUGE for generation
  • GLUE and SuperGLUE benchmarks
  • Task-specific evaluation
  • Human evaluation and limitations
Evaluation Metrics Benchmarks