AI Lesson & Submodule
Backpropagation intuition
Gradients, the chain rule, weight update loops, and learning rates.
Why This Matters
Backpropagation is the engine that allows neural networks to learn from error outputs.
What You Will Learn
- •Understanding loss gradient vectors
- •Calculating partial derivatives using the Chain Rule
- •Backpropagating error values layers-by-layers
- •Updating weights based on learning rates
Concepts Covered
Gradient VectorChain Rule MathWeight UpdatesLearning Rate scale
Mapped Foundation Project: Neural Network From Scratch
Build forward pass and backpropagation updates manually in pure Python.
Architecture Preview
System flow diagrams outlining client nodes, processing engines, and backing databases for Neural Network From Scratch.
Client WorkspaceInference GatewayNeural Network From Scratch Logic Engine
Tech Stack Planned
PythonNumPy
GitHub: Coming SoonLive Demo: Coming Soon
Coming SoonTechnical Interview Value
- ?Calculate weight updates for a simple 3-layer MLP by applying the chain rule manually