Convolutional Neural Networks

AI that sees images.

What are CNNs?

Neural networks designed for images.

Like how your brain processes visual information!

How CNNs Work

Layers:

1. Convolution: Detect features (edges, shapes)
2. Pooling: Reduce size, keep important info
3. Dense: Make final decision

Real Example - Cat vs Dog

1. Convolution: Finds edges, fur patterns
2. Pooling: Keeps key features
3. Dense: Decides "Cat" or "Dog"

Basic CNN in Python

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten

model = Sequential([
    # Convolution layer
    Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
    
    # Pooling layer
    MaxPooling2D(pool_size=(2, 2)),
    
    # Another conv + pool
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(pool_size=(2, 2)),
    
    # Flatten for dense layers
    Flatten(),
    Dense(128, activation='relu'),
    Dense(1, activation='sigmoid')  # Output: cat or dog
])

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

Convolution Explained

Slide a small filter over image:

Filter: 3x3 grid that detects patterns
Output: Feature map showing where pattern exists

Pooling Explained

Shrink image while keeping important features:

Max Pooling: Take maximum value in each region
Result: Smaller image, same key info

Famous CNN Architectures

• LeNet: Early CNN (1998)
• AlexNet: Won ImageNet (2012)
• VGG: Very deep (2014)
• ResNet: 152 layers! (2015)

Applications

• Face recognition
• Self-driving cars
• Medical image analysis
• Quality control in manufacturing

Transfer Learning

Use pre-trained models:

from tensorflow.keras.applications import VGG16

# Load pre-trained model
base_model = VGG16(weights='imagenet', include_top=False)

# Add your own layers
# Train only your layers

Remember

• CNNs are for images
• Convolution finds patterns
• Pooling reduces size
• Use transfer learning!