Phase 3: Deep Learning Core

Phase 3: Deep Learning Core

> Neural networks from first principles. No frameworks until you build one yourself.

Start this phase on GitHub

Prerequisites: Phase 1 Linear Algebra Intuition. Phase 2 is recommended
for model evaluation vocabulary.

First lesson: The Perceptron

Run this command from the repository root:

python3 phases/03-deep-learning-core/01-the-perceptron/code/perceptron.py

Keep the command, exit code, converged gate results, XOR failure of a single
perceptron, and the final two-layer XOR predictions.

Next action: Explain why one linear boundary cannot solve XOR, then
continue to Multi-Layer Networks and Forward Pass.

Browse the full Phase 3 lesson list or the
cross-phase roadmap.

Lessons in this phase

• The Perceptron
• Multi-Layer Networks and Forward Pass
• Backpropagation from Scratch
• Activation Functions
• Loss Functions
• Optimizers
• Regularization
• Weight Initialization and Training Stability
• Learning Rate Schedules and Warmup
• Build Your Own Mini Framework
• Introduction to PyTorch
• Introduction to JAX
• Debugging Neural Networks