Hyperparameter Tuning in Neural network

1. Story-Like Example: “Chef Arya and the Perfect Cake”

Imagine Arya, a passionate baker, trying to bake the perfect chocolate cake. She has a recipe, but every time the cake comes out either too dry, too sweet, or undercooked. Why?

Because Arya’s recipe has variables she needs to fine-tune:

  • Temperature of the oven (like learning rate)
  • Baking time (like number of epochs)
  • Amount of sugar (like regularization)
  • Size of baking pan (like batch size)

These are not part of the cake itself (the parameters), but the conditions and environment in which she bakes. These are Hyperparameters.

Once she starts tweaking these based on how the cake turns out (too dry → reduce temperature, too sweet → reduce sugar), she starts getting the perfect cake consistently.

In the same way, neural networks need hyperparameter tuning to perform well — it’s not just the weights (parameters), but also how we train them.

2. Key Hyperparameters in Neural Network

Hyperparameter Role in Training
Learning Rate Step size while minimizing loss
Epochs Number of times data passes through the network
Batch Size Samples per weight update
Hidden Units Network capacity to learn complexity
Activation Function Shape of decision boundary

3. When Do We Need Hyperparameter Tuning?

We may notice:

  • Loss decreasing too slowly or getting stuck.
  • Overfitting or underfitting.
  • Drastic prediction errors.
  • Validation accuracy not improving while training accuracy rises.

All these are signs that some hyperparameters are poorly chosen.

4. Story: “Ravi Learns to Drive a Car” – The Hyperparameter Journey

Ravi just joined a driving school. He wants to become a great driver and pass the driving test.
But guess what? Driving isn’t just about turning the steering or pressing the accelerator. It depends on how he learns.

Here’s what happens:

  1. Instructor’s Teaching Speed = Learning Rate

    • If the instructor talks too fast, Ravi misses important concepts (overshooting).
    • If the instructor talks too slow, Ravi gets bored and takes forever to learn.

  2. Lesson Frequency = Epochs

    • More lessons mean more practice. But too many might tire Ravi, and too few leave him unprepared.

  3. Driving Duration per Class = Batch Size

    • Longer driving sessions give more practice, but Ravi may get overwhelmed.
    • Very short sessions don’t give enough continuity.

  4. Car Type Chosen to Learn = Model Complexity

    • Starting with a racing car? Too complex.
    • A basic hatchback? Just right for beginners.

What happens when all these aren’t tuned?

  • Ravi either crashes the car, takes too long to learn, or fails the test.

But if his learning plan is adjusted carefully — slower explanations, regular practice,
manageable session lengths — Ravi becomes a confident, road-ready driver.

This is exactly how neural networks learn. Hyperparameters define
how the network learns, not what it learns.

Impact in Neural Network Terms:

Ravi’s Scenario Neural Network Equivalent Effect of Poor Tuning
Instructor talks too fast Learning Rate too high Overshoots, unstable training
Very few classes Too few epochs Underfitting, low accuracy
Driving too long in each session Large batch size Slow training or convergence
Learning on a sports car Too complex model Overfitting, poor generalization
No variation in route Poor generalization to new data Model performs poorly on new inputs

What Did Ravi’s Instructor Do?

He adjusted:

  • The pace of explanation (→ learning rate)
  • The number of practice sessions (→ epochs)
  • The driving time per day (→ batch size)

Ravi improved dramatically. Similarly, our Neural Network’s performance improves when hyperparameters are tuned to suit the data and task.

Hyperparameter Tuning in Neural network – Hyperparameter Tuning example with Simple Python