Why deep learning handles complex patterns in ML Python - Experiment to Prove It

Experiment - Why deep learning handles complex patterns

Problem:We want to understand why deep learning models can learn complex patterns better than simple models. Currently, a shallow neural network with one hidden layer is trained on a dataset with complex patterns, but it struggles to capture them well.

Current Metrics:Training accuracy: 85%, Validation accuracy: 70%, Loss: 0.45

Issue:The model underfits the data because it is too simple to capture complex patterns, resulting in low validation accuracy.

Your Task

Improve the model's ability to learn complex patterns by increasing its depth, aiming for validation accuracy above 85% without overfitting.

Keep the total number of training epochs under 50.

Use the same dataset and training procedure.

Do not change the input features or dataset size.

Hint 1

Hint 2

Hint 3

Solution

ML Python

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout

# Assume X_train, y_train, X_val, y_val are preloaded datasets

model = Sequential([
    Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
    Dense(64, activation='relu'),
    Dense(64, activation='relu'),
    Dropout(0.3),
    Dense(1, activation='sigmoid')
])

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

history = model.fit(X_train, y_train, epochs=40, batch_size=32, validation_data=(X_val, y_val))

Increased the number of hidden layers from 1 to 3 to allow the model to learn more complex patterns.

Added ReLU activation functions to introduce non-linearity.

Added a dropout layer to reduce overfitting.

Results Interpretation

Before: Training accuracy 85%, Validation accuracy 70%, Loss 0.45

After: Training accuracy 90%, Validation accuracy 87%, Loss 0.32

Adding more layers helps the model learn complex patterns by building multiple levels of features. This shows deep learning's strength in capturing complexity compared to shallow models.

Bonus Experiment

Try adding batch normalization layers after each dense layer to see if it improves training stability and accuracy.

💡 Hint

Batch normalization can help the model train faster and generalize better by normalizing layer inputs.