Quick Start Guide to Large Language Models (LLMs) ChatGPT, Llama, Embeddings, Fine–Tuning, and Multimodal AI, 2nd Edition

Introduction
Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs): Introduction
Module 1: Introduction to Large Language Models
Module Introduction
Lesson 1: Overview of Large Language Models
Topics
1.1 What Are Large Language Models?
1.2 Popular Modern LLMs
1.3 Applications of LLMs
Lesson 2: Semantic Search with LLMs
Topics
2.1 Introduction to Semantic Search
2.2 Building a Semantic Search System
2.3 Optimizing Semantic Search with Cross-Encoders and Fine-Tuning
Lesson 3: First Steps with Prompt Engineering
Topics
3.1 Introduction to Prompt Engineering
3.2 Working with Prompts Across Models
3.3 Building a Retrieval-Augmented Generation BOT with ChatGPT and GPT-4
Lesson 4: Retrieval Augmented Generation + AI Agents
Topics
4.1 Introduction to Retrival Augmented Generation (RAG)
4.2 Building a RAG bot
4.3 Using Open Source Models with RAG
4.4 Expanding into AI Agents
Module 2: Getting the Most Out of LLMs
Module Introduction
Lesson 5: Optimizing LLMs with Fine-Tuning
Topics
5.1 Transfer Learning—A Primer
5.2 The OpenAI Fine-Tuning API
5.3 Case Study: Predicting with Android App Reviews—Part 1
5.4 Case Study: Predicting with Android App Reviews—Part 2
Lesson 6: Advanced Prompt Engineering
Topics
6.1 Input/Output Validation
6.2 Batch Prompting + Prompt Chaining
6.3 Chain-of-Thought Prompting
6.4 Preventing Prompt Injection Attacks
6.5 Assessing an LLM's Encoded Knowledge Level
Lesson 7: Customizing Embeddings + Model Architectures
Topics
7.1 Case Study: Building an Anime Recommendation System
7.2 Using OpenAI's Embedded Models
7.3 Fine-tuning an Embedding Model to Capture User Behavior
Lesson 8: AI Alignment--First Principles
Topics
8.1 Introduction to AI Alignment
8.2 Evaluating Alignment Plus Ethics
Module 3: Advanced LLM Usage
Lesson 9: Moving Beyond Foundation Models
Topics
9.1 The Vision Transformer
9.2 Using Cross Attention to Mix Data Modalities
9.3 Case Study—Visual QA: Setting Up Our Model
9.4 Case Study—Visual QA: Setting Up Our Parameters and Data
9.5 Introduction to Reinforcement Learning from Feedback
9.6 Aligning FLAN-T5 with Reinforcement Learning from Feedback
Lesson 10: Advanced Open-Source LLM Fine-Tuning
Topics
10.1 BERT for Multi-label Classification—Part 1
10.2 BERT for Multi-label Classification—Part 2
10.3 Writing LaTeX with GPT-2
10.4 Case Study: Sinan's Attempt at Wise Yet Engaging Responses—Sawyer
10.5 Instruction Alignment of LLMs: Supervised Fine-Tuning
10.6 Instruction Alignment of LLMs: Reward Modeling
10.7 Instruction Alignment of LLMs: RLHF
10.8 Instruction Alignment of LLMs: Using Our Instruction-Aligned LLM
Lesson 11: Moving LLMs into Production
Topics
11.1 Cost Projecting and Deploying LLMs to Production
11.2 Knowledge Distillation
Lesson 12: LLM Evaluations
Topics
12.1 Evaluating Generative Tasks—Part 1
12.2 Evaluating Generative Tasks—Part 2
12.3 Evaluating Understanding Tasks
12.4 Probing LLMs for world model
Summary
Quick Guide to ChatGPT, Embeddings, and Other Large Language Models (LLMs): Summary







Quick Start Guide to Large Language Models (LLMs) ChatGPT, Llama, Embeddings, Fine–Tuning, and Multimodal AI, 2nd Edition Fast Download
Quick Start Guide to Large Language Models (LLMs) ChatGPT, Llama, Embeddings, Fine–Tuning, and Multimodal AI, 2nd Edition Full Download