Mastering Large Language Models: A Deep Dive

Course Description

Dive deep into the world of Large Language Models and transform your understanding of artificial intelligence. "Mastering Large Language Models" takes you on a comprehensive tour of the technologies that power today's most advanced AI systems. We begin by building a solid foundation, exploring how text is processed through tokenization and vectorization. You'll then unravel the revolutionary Transformer architecture and the self-attention mechanism that enables its remarkable capabilities.

The course quickly moves from theory to practice, equipping you with essential skills for model customization, including fine-tuning, few-shot prompting, and the powerful Retrieval Augmented Generation (RAG) technique. We explore the critical ecosystem around LLMs, including vector databases and context engineering, before pushing into the frontier of AI with Agents, Reinforcement Learning (RLHF), and Chain of Thought reasoning. Finally, you will learn to optimize these massive models for deployment using state-of-the-art distillation and quantization methods. By the end of this course, you will have a holistic and practical mastery of the LLM landscape.

Who is this course for

• Engineers who are building applications in the AI space.

• Individuals seeking a list of terms whose definitions they understand quite well.

• Team members who need to effectively communicate complex AI concepts internally or externally.

• Professionals interested in learning the deeper subjects around AI and understanding the underlying structure and inherent meaning of AI models.

Course Objectives

Upon completion of this course, you will be able to:

• Explain the end-to-end architecture of a Transformer-based Large Language Model.

• Understand and articulate the role of tokenization, vector embeddings, and the attention mechanism.

• Implement practical techniques to customize model behavior, including fine-tuning and few-shot prompting.

• Build powerful, context-aware applications using Retrieval Augmented Generation (RAG) and vector databases.

• Design and engineer effective context and prompts to steer model output reliably.

• Grasp advanced concepts such as AI Agents, Chain of Thought reasoning, and Reinforcement Learning from Human Feedback (RLHF).

• Evaluate the trade-offs between different model sizes, from massive multi-modal models to smaller, specialized SLMs.

• Apply optimization techniques like distillation and quantization to make LLMs more efficient for deployment.

Prerequisites

• Solid programming proficiency in Python.

• Experience with a major deep learning framework (e.g., PyTorch or TensorFlow).

• A strong foundation in machine learning concepts (e.g., training, validation, loss functions, backpropagation).

• Basic understanding of Natural Language Processing (NLP) concepts is beneficial but not mandatory.

Course outline

Section 1: LLM Fundamentals and Input Processing

1. Large Language Models (LLMs)

2. Tokenization

3. Vectors and Vectorization (Embeddings)

4. Attention Mechanism

Section 2: Architecture and Training

1. Transformer

2. Self-Supervised Learning

3. Fine-tuning

4. Reinforcement Learning (RL) & RLHF

Section 3: Context, Retrieval, and Interaction

1. Few-shot Prompting

2. Retrieval Augmented Generation (RAG)

3. Vector Databases

4. Model Context Protocol (MCP)

5. Context Engineering

6. Prompt/Context Summarization

Section 4: Advanced Capabilities and Optimization

1. Agents

2. Chain of Thought

3. Reasoning Models

4. Multi-modal Models

5. Small Language Models (SLMs) and Foundation Models

6. Distillation

7. Quantization

Tags

Large Language Models, LLM, Generative AI, Transformers, Attention Mechanism, RAG, Fine-Tuning, Prompt Engineering, Vector Databases, AI Agents, NLP, Deep Learning, Quantization, Reinforcement Learning

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