From Basics to Bots: My Weekly AI Engineering Adventure-14

Loss Functions - How Neural Networks Learn from Mistakes

Posted by Afsal on 29-Sep-2025

Hi Pythonistas!

In the last post we have learned about the activation function. But now comes the most important part
how does a neural network know it’s doing a good job or failing miserably? That’s where Loss Functions come in.

What is a Loss Function?
A loss function is like a teacher’s red pen. After the network makes a prediction, the loss function measures:

  • How close was the prediction to the truth?
  • By how much should we punish the network?
  • The smaller the loss, the better the model is learning.

Types of Loss Functions

1. Regression Losses (for predicting numbers)

Mean Squared Error (MSE): Squares the difference. Big mistakes hurt more.

Mean Absolute Error (MAE): Uses absolute difference. Treats all errors equally.

Huber Loss: Mixes both less sensitive to outliers than MSE, smoother than MAE.

Example: Predicting house prices.

2. Classification Losses (for categories)

Binary Cross-Entropy: Used when deciding between 2 classes (spam vs not spam).

Categorical Cross-Entropy: For multi-class problems (cat vs dog vs horse).

Sparse Categorical Cross-Entropy: Same as above but uses integer labels.

Hinge Loss: Inspired by SVMs. Works for "margin-based" classification.

Example: Identifying handwritten digits (0–9).

3. Specialized Losses

Focal Loss: Focuses on hard-to-classify examples → great for imbalanced datasets.

Dice Loss / IoU Loss: Popular in image segmentation → handles class imbalance well.

Contrastive Loss / Triplet Loss: Used for similarity tasks (like face verification).

KL Divergence: Measures difference between two probability distributions → used in VAEs and knowledge distillation.

CTC Loss: For sequence tasks where alignment is unknown (speech recognition, OCR).

Example: Detecting tumors in medical scans (Dice Loss).

How to Choose?

Regression tasks → MSE, MAE, or Huber.

Binary classification → Binary Cross-Entropy.

Multi-class classification → Categorical Cross-Entropy (with softmax).

Imbalanced data → Focal Loss, Dice Loss.

Similarity learning → Contrastive / Triplet Loss.

Probabilistic models → KL Divergence.

Speech / OCR → CTC Loss.

What I Learned

Loss functions are the compass of a neural network. Without them, the model wouldn’t know where to go.

Regression → MSE, MAE, Huber.

Classification → Cross-Entropy family.

Special tasks → Dice, Focal, Triplet, KL, CTC.

What’s Next

Next week, we’ll talk about the Optimizers the "drivers" that actually move the network in the right direction while minimizing this loss.