Practical 12: Neural Networks Basics

Objective

Build and train artificial neural networks using TensorFlow/Keras.

Duration

4-5 hours

Prerequisites

• Practicals 1-11 completed
• Understanding of deep learning basics

What You’ll Learn

• ✅ Build neural networks with Keras
• ✅ Understand activation functions
• ✅ Implement forward and backward propagation
• ✅ Train and evaluate neural networks
• ✅ Visualize network architecture

📋 Tasks

1. Build Sequential Model

import tensorflow as tf
from tensorflow import keras

model = keras.Sequential([
    keras.layers.Dense(128, activation='relu', input_shape=(10,)),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(64, activation='relu'),
    keras.layers.Dense(1, activation='sigmoid')
])

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

2. Train Model

history = model.fit(
    X_train, y_train,
    epochs=20,
    batch_size=32,
    validation_split=0.2
)

3. Evaluate

loss, accuracy = model.evaluate(X_test, y_test)
print(f"Test accuracy: {accuracy:.4f}")

predictions = model.predict(X_test)

📊 Learning Outcomes

• Design neural network architecture
• Understand layers and activation functions
• Train deep learning models
• Handle overfitting with dropout
• Evaluate neural networks