Update AI Frameworks chapter

[profvjreddi]

The current chapter on AI frameworks was written in a bit of a haste when teaching class, thinking of reworking it with the following outline.

1. Introduction to AI Frameworks

   • What are AI frameworks?
   • Importance in machine learning systems.

2. Overview of Popular Frameworks

   • Briefly introduce TensorFlow, PyTorch, and others.
   • Key features and differences.

3. Specialized Framework Variants

   • Discuss TensorFlow Lite, TensorFlow Lite Micro, etc. (could use PyTorch too!)
   • Use cases and reasons for these variants.

4. Performance Optimization

   • How frameworks leverage hardware like GPUs and TPUs.
   • Techniques for enhancing performance.
   • Show performance data

5. Ecosystem and Tooling

   • Overview of supportive tools and libraries.
   • How these tools integrate with the frameworks.

6. Interoperability and Deployment

   • Basics of model deployment and cross-framework compatibility.

7. Case Studies and Examples

   • Practical examples and real-world scenarios.