Convolutional Neural Networks (CNNs) are a category of Artificial Intelligence algorithms mainly used to analyze visual images by deep learning or machine learning. Their usage is becoming increasingly critical in applications like self-driving vehicles, facial recognition software, and many medical imaging processes.
CNNs take in raw pixel data from images as input and process them through multiple hidden layers, each designed to identify and extract increasingly complex features.
The ‘convolutional’ in ‘convolutional neural network’ refers to the mathematical operation that the network uses to process input data. This operation attempts to extract higher-level features like edges, corners, and so on, which are then used for image recognition.
Here’s an example of how this operation works. The code creates a convolution kernel which is then applied to an input image using the Keras API in Python.
# Python Code # Import necessary library from keras.models import Sequential from keras.layers import Conv2D # Create a model model = Sequential() # Add a Convolutional Layer to the model model.add(Conv2D(1, (3,3), input_shape=(5, 5, 3))) # Print the model summary model.summary()
Poolings layers are usually added after each convolutional layer in a CNN. The main task of a pooling layer is to reduce the spatial dimensions, or size, of the convolved feature - helping to decrease computational complexity.
Let’s define a pooling layer in the same model as previously using Python.
# Python Code # Import necessary library from keras.layers import MaxPooling2D # Add pooling layer model.add(MaxPooling2D(pool_size=(2,2))) # Print the model summary model.summary()
Convolutional Neural Networks have been particularly successful in image and video recognition tasks. Their ability to deal with high dimensional data in a robust and invariant manner has made them a mainstay in the field of machine learning.
As our understanding and technology continue to grow, the applications of CNNs will undoubtedly become even more impressive and extensive.