Regularisation for Deep models

Mon, 01 Jan 0001 00:00:00 +0000

Regularisation for Deep models #

Regularisation means adding constraints or techniques that prevent a model from becoming too complex and memorising the training data.

The goal is not only low training error.

The goal is good performance on unseen data.

Key takeaway:
Regularisation helps the model generalise by controlling complexity, stabilising training, and reducing overfitting.

Generalization for regression
Training Error and Generalization Error
Underfitting or Overfitting
Model Selection
Weight Decay and Norms
Generalization in Classification
Environment and Distribution Shift
Generalization in Deep Learning
Dropout
Batch Normalization
Layer Normalization

Underfitting, Good Fit, and Overfitting ☆ #

Case	Model behaviour	Training error	Test error
Underfitting	too simple	high	high
Good fit	captures useful pattern	low	low
Overfitting	memorises training noise	very low	high

flowchart LR
 A["Model Complexity"] --> B["Too Simple: Underfitting"]
 A --> C["Just Right: Good Fit"]
 A --> D["Too Complex: Overfitting"]

 style A fill:#E1F5FE,stroke:#4A90E2
 style B fill:#FFF9C4,stroke:#FBC02D
 style C fill:#C8E6C9,stroke:#43A047
 style D fill:#EDE7F6,stroke:#7E57C2

Training Error and Generalisation Error ☆ #

Training error measures performance on data used for learning.

Regularisation on Arshad Siddiqui

Regularisation for Deep models

Regularisation for Deep models #

Underfitting, Good Fit, and Overfitting ☆ #

Training Error and Generalisation Error ☆ #