Loss Function relevancy in Neural Network

1. What is a Loss Function in a Neural Network?

In Simple Terms:

Imagine we’re teaching a robot how to throw a ball into a basket.It throws the ball.

We measure how far it missed the basket.We give it feedback: “You missed by this much!”

The robot learns from this feedback and adjusts the next throw.

That “how far it missed” is exactly what a loss function does — it quantifies the error between the predicted value and the actual (true) value.

2. Why is the Loss Function Important?

Because it drives the learning in a neural network.It tells the network how wrong it is.The network uses that to adjust its internal settings (weights and biases) to improve.Without a loss function, the network wouldn’t know what to fix or how much to adjust.

How It Works in Training:

Each time the neural network makes a prediction:

It compares the prediction to the true answer.
The loss function calculates a number (the loss).
This number is used by backpropagation to adjust the weights.
Over time (across many epochs), the loss gets smaller — the model gets smarter.

Common Types of Loss Functions

Problem Type	Loss Function	What It Does
Regression	Mean Squared Error (MSE)	Penalizes big errors more harshly (squares the error)
Regression	Mean Absolute Error (MAE)	Just takes the absolute difference
Classification	Cross Entropy Loss	Measures the distance between two probability distributions
Binary Classification	Binary Cross Entropy	Handles 0/1 classification

3. Example: Loss Function in Math

Suppose we are predicting scores:

Actual score = 90
Predicted score = 70
Loss (MSE) = (90 – 70)² = 400

Higher the loss → more error → bigger weight adjustments needed.

Visualization Idea:

Imagine a bowl-shaped curve — like a valley. The bottom of the valley is the minimum loss.

The optimizer (like Gradient Descent) rolls the ball down the valley — using the loss function to guide which way to go.

Loss Function relevancy in Neural Network – Loss Function with Simple Python