12. What is a convolutional neural network (CNN) and how is it used in image processing?

Convolutional Neural Networks (CNNs)

Convolutional Neural Networks, or CNNs, are a class of deep neural networks that have proven highly effective for processing structured grid data, such as images. They are particularly well-suited for image recognition and classification tasks due to their ability to capture spatial hierarchies in the data.

How CNNs Work

CNNs consist of several layers, each performing specific operations:

1. Convolutional Layers: These layers apply convolution operations to the input data, using filters (or kernels) to extract features such as edges, textures, and patterns. The result is a set of feature maps.

   import tensorflow as tf
   
   model = tf.keras.Sequential([
       tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(64, 64, 3)),
       tf.keras.layers.MaxPooling2D((2, 2)),
       tf.keras.layers.Flatten(),
       tf.keras.layers.Dense(64, activation='relu'),
       tf.keras.layers.Dense(10, activation='softmax')
   ])

2. Pooling Layers: Pooling operations, such as max pooling, reduce the dimensionality of each feature map while retaining the most important information. This makes the model less sensitive to the position of features in the input image.

3. Fully Connected Layers: After several convolutional and pooling operations, the high-level reasoning in the neural network is done via fully connected layers. These layers output the final predictions.

Applications in Image Processing

CNNs are widely used in various image processing tasks, including:

• Image Classification: Assigning a label to an image, such as identifying objects or scenes.
• Object Detection: Locating objects within an image, often with bounding boxes.
• Image Segmentation: Dividing an image into segments to simplify analysis.
• Face Recognition: Identifying or verifying individuals in a given image.

CNNs are foundational to many modern computer vision applications and continue to evolve with advancements in neural network architectures.