From tensors to consciousness with Apple's MLX

A comprehensive journey through the theoretical foundations of artificial intelligence and machine learning, implemented using Apple's MLX framework on Apple Silicon.

This repository contains a systematic exploration of computational theory, progressing through 7 levels:

0. Computational Primitives - Tensors, operations, reductions
1. Automatic Differentiation - Chain rule, gradients, backpropagation theory
2. Optimization Theory - Gradient descent, momentum, adaptive methods
3. Neural Network Theory - Universal approximation, information flow
4. Advanced Theory - Manifold learning, attention mechanisms, Riemannian optimization
5. Research Frontiers - Meta-learning, scaling laws, lottery tickets, grokking
6. Theoretical Limits - Information geometry, consciousness, quantum computation

• Apple Silicon Mac (Metal GPU acceleration)
• Python 3.11+
• MLX framework
• Virtual environment recommended

then

Run the scripts in sequence:

• Modern AI emerges from elegant mathematical progressions
• Each level builds systematically on previous foundations
• Demonstrates the power of Apple Silicon and MLX for ML research
• Bridges multiple fields: mathematics, physics, neuroscience, computer science