Materials
Beginner
3 Chapters · 35 Lessons
Introduction to AI & Fine-Tuning Basics
My Courses
Chapter 1
Mastering the Basics
Lessons
Transforming AI for Custom Tasks (Fine-Tuning)
How Do They Differ? (Fine-Tuning vs Prompt Engineering)
What are the Benefits of Fine-tuning?
Experience a Fine-Tuned Model with MBTI
Fine-Tuned Model Specializing in Tabular Data
Multiple-choice quiz
A Beginners Guide to AI (Machine Learning Terminology)
What is Deep Learning?
How Do Machine Learning and Deep Learning Differ?
What Does It Mean for AI to Learn?
How Does Fine-Tuning Work?
Fill-in-the-blank quiz
Fine-Tuning Practice
Chapter 2
Preparing Learning Data
Lessons
Data Formats Used for Training AI
Deep Dive into JSON Data Format
Fuel for Fine-Tuning (JSONL)
JSONL Format for Fine-Tuning
Preparing to Train AI (Dataset Essentials)
Types of Datasets Used in AI Training
How to Split a Dataset
Preparing Data for Consumption (Preprocessing)
What Data Formats Do Different AI Models Use?
Creating a JSONL Dataset
Comparing Responses of General vs Fine-tuned Models
Multiple-choice quiz
Chapter 3
Deciding How to Train
Lessons
Distance Between Ideal and Reality (Loss Function)
Gradient of the Loss Function
Deciding How to Train AI (Hyperparameters)
Just Remember Three (Learning Rate, Batch Size, Epochs)
Learning Speed and Learning Rate
The Scale of Data Processed at Once (Batch Size)
Number of Epochs in Training
Comparing Hyperparameters Across Fine-tuning Models
Fill-in-the-blank quiz
Summary of Course Highlights