Neural Network Project

🎯 Project: Complete Neural Network Implementation

This project will help you apply everything you've learned about neural networks. You'll design a network architecture, implement forward propagation, calculate loss, simulate backpropagation, and train a model.

This project combines all the neural network concepts: architecture design, activation functions, forward pass, loss calculation, and weight updates.

Neural Network Components

A complete neural network implementation includes several key components:

network_components.py
# Neural Network Components

print("Complete Neural Network Implementation:")
print("=" * 50")

print("\n1. Network Architecture:")
print("   - Input layer size (number of features)")
print("   - Hidden layers and sizes")
print("   - Output layer size")

print("\n2. Weights and Biases:")
print("   - Initialize weights (random values)")
print("   - Initialize biases (usually zeros)")

print("\n3. Forward Propagation:")
print("   - Pass input through network")
print("   - Apply weights, add biases")
print("   - Apply activation functions")

print("\n4. Loss Calculation:")
print("   - Compare prediction with target")
print("   - Calculate error")

print("\n5. Backpropagation & Training:")
print("   - Calculate gradients")
print("   - Update weights")
print("   - Repeat for multiple epochs")

Example: Binary Classification Network

Let's build a network for binary classification:

binary_classifier.py
# Binary Classification Neural Network

print("Binary Classification Network Example:")
print("=" * 50")

print("\nArchitecture:")
print("  Input: 4 features")
print("  Hidden 1: 8 neurons (ReLU activation)")
print("  Hidden 2: 4 neurons (ReLU activation)")
print("  Output: 1 neuron (Sigmoid activation)")

print("\nTraining Process:")
print("  1. Forward pass: input → hidden1 → hidden2 → output")
print("  2. Calculate loss: binary_crossentropy")
print("  3. Backpropagate errors")
print("  4. Update weights")
print("  5. Repeat for all training examples")

print("\nExpected Results:")
print("  - Loss decreases over epochs")
print("  - Accuracy improves")
print("  - Model learns patterns in data")

Exercise: Complete Neural Network Project

Complete the exercise on the right side:

Task 1: Design network architecture (input, hidden layers, output)
Task 2: Implement forward propagation through all layers
Task 3: Calculate loss for predictions
Task 4: Simulate weight updates using gradient descent
Task 5: Track training progress (loss over epochs)

Write your code to build and train a complete neural network!

💡 Project Tips

Break the project into smaller tasks. Complete and test each part before moving to the next. Don't try to do everything at once—iterative development leads to better results!

🎉

Lesson Complete!

Great work! Continue to the next lesson.