Here’s the Version 2: Hard Mode — research-grade neural network design skills curriculum.

This does not replace the earlier one; it builds on top of it.

Purpose:

• Not just fixing bad models → but inventing better ones.
• Going beyond "train this" to "understand the why" and "design new ideas."
• Moving you toward Staff Engineer, Research Engineer, or even applied researcher level in neural networks.

Advanced Neural Network Architecture Plan (Hard Mode)

Phase R0: Theory Bedrock — Mathematical Intuition First

Goal: Know why architectures behave the way they do mathematically.

Resources:

• Deep Learning Book by Ian Goodfellow — Chapters 6, 7, 8 (only those)
• Stanford CS231n Lecture Notes (2022)
• Understanding Overparameterization (this blog is killer)

Exercises:

• Derive on paper:
  • Why do deep networks need skip connections?
  • Why does BatchNorm stabilize training mathematically?
• Solve:
  • Recreate basic experiments that show exploding/vanishing gradients.

Deliverable:

• 1 writeup: "The math behind why ResNets work."