Contents
📘 Part 1: Foundations of Language Models
What is a Language Model?
History of LLMs: From N-Grams to GPT
How Do LLMs Understand Text?
Tokens and Tokenization Explained
Embeddings: Turning Words into Numbers
Neural Networks 101 (with a focus on Transformers)
What is a Transformer Architecture?
The Role of Attention and Self-Attention
How Do LLMs Generate Text?
Decoding Strategies: Greedy, Beam Search, Sampling
⚙️ Part 2: Training and Fine-Tuning
What is Pretraining?
Common Training Datasets (Wikipedia, BooksCorpus, etc.)
Transfer Learning in NLP
Fine-Tuning vs Prompt Engineering
Introduction to LoRA, PEFT, and QLoRA
Training Loss Functions in LLMs
What is a Foundation Model?
Ethics in Data Collection and Training
Costs of Training an LLM
Why Do LLMs Hallucinate?
🔍 Part 3: Prompting and Use
What is Prompt Engineering?
Few-Shot, Zero-Shot, and One-Shot Learning
Prompt Templates for Common Tasks (Summarization, Q&A, etc.)
How to Evaluate LLM Responses
Chain-of-Thought Prompting
Role of Temperature and Top-k Sampling
Using LLMs for Code Generation
Using LLMs for Text Summarization
LLMs as Agents and Tool Users
Guardrails and Safety Filters
🧠 Part 4: Hands-On with LLMs
How to Use OpenAI (ChatGPT, GPT-4) APIs
Using Hugging Face Transformers
Exploring LLMs in LangChain
Building a Q&A Bot with LLMs
Connecting LLMs to Google Docs / PDFs
Running LLMs Locally (GGUF, Ollama, LM Studio)
Vector Databases and RAG (Retrieval-Augmented Generation)
Streamlit/FastAPI + LLM Demo Projects
Integrating LLMs in Web Applications
Cost Optimization for LLM Use
🌐 Part 5: Broader Perspectives
The Future of Language Models
Multimodal Models: Text + Image + Audio
Comparing GPT, Claude, Gemini, Mistral, and LLaMA
Open Source vs Closed Source LLMs
LLMs in Education and Research
Business Use Cases of LLMs
Legal, Copyright, and Bias Issues
Responsible AI and Safety in LLMs
How to Stay Updated in the LLM World
Career Paths in LLM and Generative AI
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