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

```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:

```python 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!