Skip to main content

Regularization in AI Training [say to prevent overfitting]

 

Regularization in AI Training

Regularization is a set of techniques used in machine learning and deep learning to prevent overfitting by adding constraints or penalties to the model during training. It helps improve the model's ability to generalize to new, unseen data.

Why is Regularization Needed?

When training AI models, the goal is to learn patterns that generalize well to new data. However, models, especially deep neural networks, can become too complex and start memorizing the training data instead of learning meaningful patterns. This leads to overfitting, where the model performs well on the training data but poorly on validation or test data.

Regularization helps control model complexity and reduce overfitting by discouraging it from learning overly complex or specific patterns that do not generalize.

Common Regularization Techniques

1. L1 and L2 Regularization (Weight Decay)

These are the most common forms of regularization applied to model weights.

  • L1 Regularization (Lasso Regression):

    • Adds the absolute values of the weights as a penalty to the loss function.

    • Encourages sparsity, meaning some weights become exactly zero, effectively selecting only the most important features.

    • Formula:

      Lregularized=L+λ∑∣wi∣L_{\text{regularized}} = L + \lambda \sum |w_i|

    • Used when feature selection is desired.

  • L2 Regularization (Ridge Regression / Weight Decay):

    • Adds the squared values of the weights as a penalty.

    • Encourages smaller, more evenly distributed weights but does not force them to be exactly zero.

    • Formula:

      Lregularized=L+λ∑wi2L_{\text{regularized}} = L + \lambda \sum w_i^2

    • Helps reduce the impact of any single feature.

  • Elastic Net Regularization:

    • A combination of L1 and L2 regularization.

    • Useful when working with high-dimensional data with correlated features.

2. Dropout (Neural Networks)

  • A technique specific to deep learning.

  • Randomly "drops out" (deactivates) a fraction of neurons during training to prevent co-adaptation.

  • Forces the network to learn more robust and generalizable features.

  • At inference time, dropout is disabled, but neuron outputs are scaled accordingly.

3. Early Stopping

  • Stops training when the validation loss starts increasing, indicating overfitting.

  • Prevents the model from continuing to learn noise in the data.

4. Batch Normalization

  • Normalizes the inputs of each layer to prevent extreme weight updates.

  • Reduces dependency on weight initialization and acts as a form of regularization.

5. Data Augmentation

  • Instead of modifying the model, this method modifies the training data.

  • Introduces variations (e.g., flipping, rotating, cropping images) to increase dataset diversity.

  • Helps the model generalize better without learning irrelevant patterns.

6. Noise Injection

  • Adding small amounts of noise to inputs or model weights to make training more robust.

  • Can be applied to images, text embeddings, or numerical data.

7. Constraint-based Regularization

  • Max-Norm Regularization: Constrains the maximum norm of weight vectors.

  • Spectral Normalization: Regularizes based on singular value decomposition (SVD).

When to Use Regularization?

  • If the model is overfitting (good training performance but poor validation/test performance).

  • When working with small datasets to prevent memorization.

  • For deep learning models, dropout and batch normalization are common choices.

Key Takeaways

  • Regularization helps models generalize better to unseen data.

  • L1 leads to feature selection (sparse weights), while L2 leads to smaller weights (smooth generalization).

  • Dropout, early stopping, and batch normalization are common deep learning techniques.

  • Data augmentation and noise injection add robustness without modifying the model itself.


Comments

Post a Comment

Popular posts from this blog

Simple Linear Regression - and Related Regression Loss Functions

Today's Topics: a. Regression Algorithms  b. Outliers - Explained in Simple Terms c. Common Regression Metrics Explained d. Overfitting and Underfitting e. How are Linear and Non Linear Regression Algorithms used in Neural Networks [Future study topics] Regression Algorithms Regression algorithms are a category of machine learning methods used to predict a continuous numerical value. Linear regression is a simple, powerful, and interpretable algorithm for this type of problem. Quick Example: These are the scores of students vs. the hours they spent studying. Looking at this dataset of student scores and their corresponding study hours, can we determine what score someone might achieve after studying for a random number of hours? Example: From the graph, we can estimate that 4 hours of daily study would result in a score near 80. It is a simple example, but for more complex tasks the underlying concept will be similar. If you understand this graph, you will understand this blog. Sim...
Post a Comment
Read more

What problems can AI Neural Networks solve

How does AI Neural Networks solve Problems? What problems can AI Neural Networks solve? Based on effectiveness and common usage, here's the ranking from best to least suitable for neural networks (Classification Problems, Regression Problems and Optimization Problems.) But first some Math, background and related topics as how the Neural Network Learn by training (Supervised Learning and Unsupervised Learning.)  Background Note - Mathematical Precision vs. Practical AI Solutions. Math can solve all these problems with very accurate results. While Math can theoretically solve classification, regression, and optimization problems with perfect accuracy, such calculations often require impractical amounts of time—hours, days, or even years for complex real-world scenarios. In practice, we rarely need absolute precision; instead, we need actionable results quickly enough to make timely decisions. Neural networks excel at this trade-off, providing "good enough" solutions in seco...
Post a Comment
Read more

Activation Functions in Neural Networks

  A Guide to Activation Functions in Neural Networks 🧠 Question: Without activation function can a neural network with many layers be non-linear? Answer: Provided at the end of this document. Activation functions are a crucial component of neural networks. Their primary purpose is to introduce non-linearity , which allows the network to learn the complex, winding patterns found in real-world data. Without them, a neural network, no matter how deep, would just be a simple linear model. In the diagram below the f is the activation function that receives input and send output to next layers. Commonly used activation functions. 1. Sigmoid Function 2. Tanh (Hyperbolic Tangent) 3. ReLU (Rectified Linear Unit - Like an Electronic Diode) 4. Leaky ReLU & PReLU 5. ELU (Exponential Linear Unit) 6. Softmax 7. GELU, Swish, and SiLU 1. Sigmoid Function                       The classic "S-curve," Sigmoid squashes any input value t...
Post a Comment
Read more