This page provides an overview of Convolutional Neural Networks (CNNs) and their application in image classification. If you are looking to dive deeper into the subject, be sure to check out our Deep Learning Fundamentals course.

What is CNN?

Convolutional Neural Networks (CNNs) are a class of deep neural networks that are particularly effective for image recognition and classification tasks. They are designed to automatically and adaptively learn spatial hierarchies of features from input images.

Key Features of CNNs

  • Convolutional Layers: These layers perform convolution operations, which help the network to capture spatial hierarchies of features from the input images.
  • Pooling Layers: These layers reduce the spatial dimensions of the input, which helps to reduce computational complexity and memory usage.
  • Fully Connected Layers: These layers connect every neuron in the previous layer to every neuron in the current layer, allowing the network to learn complex patterns.

CNN Image Classification

CNNs are widely used for image classification tasks, such as identifying objects in images, classifying images into categories, and more. Here are some popular CNN architectures used for image classification:

  • LeNet-5: One of the earliest CNN architectures, which was used for handwritten digit recognition.
  • AlexNet: Introduced deep convolutional networks and significantly improved the performance of image classification.
  • VGGNet: Known for its simplicity and effectiveness, VGGNet uses a stack of convolutional and pooling layers.
  • ResNet: Introduced the concept of residual learning, which allows for the training of very deep networks.

Example: CNN for Image Classification

Let's take a look at an example of how a CNN can be used for image classification:

Input Image

Example Input Image

CNN Architecture

  • Convolutional Layer: Extracts features from the input image.
  • Pooling Layer: Reduces the spatial dimensions of the feature map.
  • Fully Connected Layer: Classifies the image into one of the predefined categories.

Output

The output of the CNN is a probability distribution over the different classes. The class with the highest probability is considered as the predicted class.

Further Reading