Welcome to the Frequently Asked Questions (FAQs) repository for the AI Engineering program. This repository serves as a centralized knowledge base for students, instructors, and anyone interested in the curriculum, tools, and best practices covered in the program.
This project aims to collect and organize common questions and answers related to the core pillars of AI Engineering:
- Hardware Acceleration: Understanding the role of GPUs and CUDA in AI.
- Programming Language: Why Python is the de facto standard for AI.
- Development Environment: Setting up and using remote development tools effectively.
This repository is designed to be a self-paced user manual for the AI Engineering program. You can navigate it in two ways:
- Sequential Reading: Start from the basics of Hardware and move towards Software and Development Environment.
- Reference Guide: Use the detailed topic links below to jump directly to the specific answer you need when you encounter a problem or concept gap.
The FAQs cover three major topics. Click on the sub-topics to navigate directly to that section.
1. GPU & CUDA
- The Role of Matrix Multiplication in AI
- Why GPUs are Superior for AI
- What is CUDA?
- Version Compatibility & Hardware Verification
2. Python
- Why Python is the Dominant Language in AI
- Key Features Beneficial for AI Development
- Commonly Used AI Libraries & Frameworks
- Managing Installations and Dependencies
This project is licensed under the Apache 2.0 License - see the LICENSE file for details.