Convolutional Neural Network

term_id: convolutional_neural_network

Category: basic_concepts

Definition

Convolutional Neural Networks (CNNs) are designed to automatically and adaptively learn spatial hierarchies of features from visual inputs. They utilize convolutional layers that apply filters to detect local patterns like edges, textures, and shapes. Through pooling and fully connected layers, CNNs reduce dimensionality and extract high-level abstractions, making them highly effective for image classification, object detection, and segmentation tasks where spatial relationships are critical.

Summary

A specialized class of deep neural networks primarily used for processing grid-like data, such as images, by applying convolutional filters.

Key Concepts

  • Convolutional Layers
  • Pooling
  • Feature Maps
  • Spatial Hierarchy

Use Cases

  • Image classification
  • Object detection in video streams
  • Medical image diagnosis

Code Example

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import tensorflow as tf
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10)
])

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