Large Language Models

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LLM Architecture

1
Introduction to Large Language Models
18 min
FREE
2
How LLMs Work: Core Concepts
22 min
FREE
3
Tokenization and Vocabulary
20 min

Transformers & Attention

4
The Transformer Architecture
28 min
5
Self-Attention and Multi-Head Attention
26 min
6
Positional Encoding and Embeddings
22 min

Popular LLM Models

7
GPT Series: From GPT-1 to GPT-4
24 min
8
Claude, Gemini, and Other Models
22 min
9
Open Source LLMs: Llama, Mistral, and More
25 min

LLM APIs & Integration

10
Working with LLM APIs
20 min
11
Streaming and Token Management
22 min
12
Building LLM-Powered Applications
30 min

Introduction to Large Language Models

Welcome to Large Language Models! This course provides an in-depth exploration of LLMs - the technology powering modern AI assistants like ChatGPT, Claude, and Gemini. You'll learn how these powerful models work under the hood.

What are Large Language Models?

Large Language Models (LLMs) are advanced neural networks trained on vast amounts of text data to understand and generate human-like text. They use the transformer architecture and can perform a wide range of language tasks including translation, summarization, question-answering, and code generation.

Key Characteristics of LLMs

  • Scale - Trained on billions to trillions of parameters
  • Generality - Can perform multiple tasks without task-specific training
  • Context Understanding - Comprehend and maintain long-range dependencies
  • Few-Shot Learning - Learn new tasks from just a few examples
  • Emergent Abilities - Display capabilities not explicitly programmed

The Transformer Revolution

The breakthrough that enabled LLMs was the transformer architecture introduced in the "Attention is All You Need" paper (2017). Key innovations include:

  • Self-Attention Mechanism - Allows models to weigh the importance of different words in context
  • Parallel Processing - Unlike RNNs, transformers can process entire sequences simultaneously
  • Scalability - Architecture scales efficiently with more data and parameters
  • Transfer Learning - Pre-trained models can be fine-tuned for specific tasks

Popular LLM Families

The LLM landscape includes several major model families:

  • GPT Series - OpenAI's generative pre-trained transformers (GPT-3.5, GPT-4)
  • Claude - Anthropic's constitutional AI models
  • Gemini - Google's multimodal AI models
  • Llama - Meta's open-source LLM family
  • Mistral - High-performance open-source models
  • PaLM - Google's Pathways Language Model

How LLMs Learn

LLMs are trained through a multi-stage process:

  • Pre-training - Learning language patterns from massive text corpora
  • Fine-tuning - Adapting to specific tasks or domains
  • RLHF - Reinforcement Learning from Human Feedback for alignment
  • Instruction Tuning - Training to follow user instructions

Real-World Applications

LLMs are transforming industries:

  • Software Development - Code generation, debugging, and documentation
  • Content Creation - Writing, editing, and creative work
  • Customer Service - Intelligent chatbots and support systems
  • Education - Personalized tutoring and learning assistance
  • Research - Literature review, data analysis, and hypothesis generation
  • Healthcare - Medical documentation and diagnostic assistance

What You'll Master

Throughout this comprehensive course, you'll explore:

  • Transformer architecture and attention mechanisms
  • Tokenization, embeddings, and positional encoding
  • Training techniques and optimization strategies
  • Comparison of major LLM models and their capabilities
  • Working with LLM APIs and SDKs
  • Building production-ready LLM applications
  • Best practices for token management and cost optimization

Prerequisites

  • Basic understanding of machine learning concepts
  • Familiarity with neural networks
  • Python programming experience
  • Understanding of NLP fundamentals (helpful but not required)

By the end of this course, you'll have deep knowledge of how LLMs work and practical skills to build applications powered by these revolutionary models.

Let's dive into the world of Large Language Models!