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Deep Learning / 06

Backpropagation

Trace loss gradients through a network and distinguish gradient computation from parameter updates.

Section 6 of 32 · 14 subtopics

This is a knowledge index. Published titles open their articles; the remaining entries outline planned coverage.

From prediction to parameter update

Forward pass

  1. Inputs
  2. Network
  3. Prediction
  4. Loss

Backward pass

  1. Loss
  2. Chain rule
  3. Parameter gradients

Update

  1. Gradients
  2. Optimizer
  3. Updated parameters
  4. Next forward pass

Backpropagation computes gradients. The optimizer uses them to update parameters for the next forward pass; neither step guarantees better performance on unseen data.

Deep Learning / deep dive

Simple operations. Learned representations.

Follow a signal through a neural network, then follow the error back to understand what training changes.

Subtopics

  1. 06.01

    What is Backpropagation?

    Planned guide: What is Backpropagation. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  2. 06.02

    Error Signal

    Planned guide: Error Signal. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  3. 06.03

    Chain Rule in Neural Networks

    Planned guide: Chain Rule in Neural Networks. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  4. 06.04

    Local Gradients

    Planned guide: Local Gradients. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  5. 06.05

    Gradient Flow

    Planned guide: Gradient Flow. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  6. 06.06

    Weight Gradients

    Planned guide: Weight Gradients. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  7. 06.07

    Bias Gradients

    Planned guide: Bias Gradients. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  8. 06.08

    Backpropagation Through Layers

    Planned guide: Backpropagation Through Layers. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  9. 06.09

    Parameter Updates

    Planned guide: Parameter Updates. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  10. 06.10

    Backpropagation Example

    Planned guide: Backpropagation Example. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  11. 06.11

    Automatic Differentiation

    Planned guide: Automatic Differentiation. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  12. 06.12

    Vanishing Gradients

    Planned guide: Vanishing Gradients. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  13. 06.13

    Exploding Gradients

    Planned guide: Exploding Gradients. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation
  14. 06.14

    Gradient Checking

    Planned guide: Gradient Checking. Focus on intuition, assumptions, and a worked example.

    beginnerArticle in preparation