Hyperparameter Tuning in Deep Learning

In the previous lesson, we learned how a neural network is trained and how learning progresses across epochs.

Now we move into one of the most important and practical skills in deep learning — hyperparameter tuning.

In real-world deep learning projects, the difference between a poor model and a high-performing model is often not the architecture, but how well the hyperparameters are chosen.

What Are Hyperparameters?

Hyperparameters are values that are not learned by the neural network during training. Instead, they are set by the engineer before training begins.

They control how the model learns, not what it learns.

Examples include learning rate, batch size, number of layers, number of neurons, activation functions, and regularization strength.

A neural network with the same data and architecture can behave very differently under different hyperparameter settings.

Model Parameters vs Hyperparameters

It is important to clearly separate these two ideas.

Model parameters are learned automatically by the network. These include weights and biases.

Hyperparameters are chosen manually and remain fixed during training.

The network learns parameters. You, as the engineer, control hyperparameters.

Why Hyperparameter Tuning Is Critical

Deep learning models are highly sensitive to hyperparameters.

A learning rate that is too high can cause the loss to explode. A learning rate that is too low can make training painfully slow.

Similarly, a batch size that is too small may lead to noisy updates, while a very large batch size may prevent the model from generalizing well.

This is why professional deep learning workflows always include systematic tuning.

Real-World Example

Imagine training a neural network to predict customer churn.

If the learning rate is too high, the model may keep overshooting the optimal solution and never converge.

If the learning rate is too low, training may take days without significant improvement.

The dataset is the same. The architecture is the same. Only the hyperparameter choice changes the outcome.

Using Our Deep Learning Dataset

From this lesson onward, we will begin using a real dataset to make our learning practical.

You can download the dataset here:

⬇ Download Deep Learning Practice Dataset

This dataset is designed to support classification, regression, and sequence-based tasks throughout this module.

Loading the Dataset

import pandas as pd

df = pd.read_csv("dataplexa_deep_learning_master_dataset.csv")
df.head()

At this stage, we focus only on understanding how hyperparameters affect training behavior, not on perfect feature engineering.

Key Hyperparameters We Tune First

In practice, we do not tune everything at once.

We usually start with the most impactful hyperparameters:

Learning rate controls how large each update step is. Batch size controls how many samples are processed at once. Number of epochs controls how long training continues.

These three alone can dramatically change training results.

Example: Changing Learning Rate

from tensorflow.keras.optimizers import Adam
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

model = Sequential([
    Dense(64, activation="relu", input_shape=(df.shape[1]-1,)),
    Dense(1, activation="sigmoid")
])

optimizer = Adam(learning_rate=0.001)

model.compile(
    optimizer=optimizer,
    loss="binary_crossentropy",
    metrics=["accuracy"]
)

Even a small change in the learning rate can completely alter the training curve.

Manual vs Automated Tuning

There are two main approaches to hyperparameter tuning.

Manual tuning relies on experience, intuition, and observation. This is where beginners should start.

Automated tuning uses systematic search strategies such as grid search, random search, or Bayesian optimization.

We will explore automated tuning methods later in this module, once the foundations are solid.

Common Beginner Mistakes

A very common mistake is tuning too many hyperparameters at once.

Another mistake is changing hyperparameters without tracking results, making it impossible to know what actually helped.

Professional workflows always tune gradually and record experiments.

Mini Practice

Think about the following:

If your model is learning very slowly but steadily, which hyperparameter would you try adjusting first?

Exercises

Quick Quiz

In the next lesson, we will connect hyperparameters to epochs, batch size, and learning rate schedules, and observe how training dynamics change in practice.