6S191_MIT_DeepLearning

# Understanding Convolutional Neural Networks (CNNs)

CNNs are a class of deep neural networks designed to process data that has a grid-like topology, such as images. They are particularly useful in computer vision tasks.

## Key Concepts
- **Convolutional Layers**: Apply filters to extract features from input data.
- **Pooling Layers**: Reduce the spatial dimensions of the data, helping to reduce computation and prevent overfitting.
- **Fully Connected Layers**: Used at the end of the network to make final predictions.

## Practical Example: Image Classification with TensorFlow

```python
import tensorflow as tf
from tensorflow.keras import layers, models

# Load and preprocess the CIFAR-10 dataset
train_images, train_labels = tf.keras.datasets.cifar10.load_data()[0]
train_images = train_images / 255.0

# Define the CNN model
model = models.Sequential([
    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.MaxPooling2D((2, 2)),
    layers.Conv2D(64, (3, 3), activation='relu'),
    layers.Flatten(),
    layers.Dense(64, activation='relu'),
    layers.Dense(10)
])

# Compile and train the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])
model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels))
```