Python Classes for AI Model Subclassing and Fine-tuning

Overview

Master Python classes and their application in AI development, focusing on LLM fine-tuning and Vision transformer implementation with KERAS3 functionality.

Syllabus

• Introduction to Python Object-Oriented Programming
Overview of Python classes and objects
Attributes and methods
Inheritance and polymorphism
• Advanced Python Class Features
Class and static methods
Property decorators
Dunder methods for operator overloading
• Python Classes for AI Development
Designing reusable classes for AI tasks
Understanding class hierarchies in AI models
Implementing model parameters as class attributes
• Introduction to KERAS3 and its Functional API
Overview of KERAS3 functionalities
Building models using the Functional API
Advantages of subclassing in KERAS3
• Subclassing in Large Language Models (LLMs)
Creating custom layers for LLMs
Fine-tuning pre-trained LLMs with subclassing
Case study: Implementing and fine-tuning a GPT-like model
• Fine-tuning Vision Transformers (ViT) using Python Classes
Overview of Vision Transformers architecture
Building and customizing ViT using KERAS3
Practical example: Fine-tuning a ViT on a custom dataset
• Best Practices and Optimization
Strategies for efficient model subclassing
Managing resources and optimizing performance
Debugging and testing custom AI model classes
• Capstone Project: Implementing a Custom AI Model
Define a project scope using Python classes
Design, implement, and fine-tune a custom AI model
Present and evaluate your AI model
• Conclusion and Further Resources
Review key concepts
Recommended readings and resources for further learning

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Subjects

Programming