What are convolutional neural networks and how they work?

Convolutional Neural Networks (CNNs), a Deep Learning algorithm, take an input image, process it, and classify it into various aspects in the image. As a class of artificial neural networks (ANNs) that lead to various computer vision tasks, CNN is attracting interest across diverse domains, including radiology. It can effectively capture the Spatial and Temporal dependencies in an image with the help of the application of relevant filters. It also performs a better fitting to the image dataset owing to the decrease in the number of parameters involved and the reusability of weights.

In Convolutional Neural Networks, convolution is the first layer to excerpt features from an input image. It is a mathematical operation, creating a set of weights and essentially making a representation of parts of the image. Convolution of an image with distinct filters can perform operations like edge detection, blur, and sharpen by applying filters.

Understanding Convolutional Neural Networks

CNN's are a significant tool for most machine learning practitioners today. However, understanding these networks and learning to use them for the first time can be a challenge. One of the very first convolutional neural networks was LeNet, which helped propel the field of Deep Learning. Pioneered by Yann LeCun, this network was named LeNet5 after many previous successful iterations since the year 1988. The LeNet architecture then was used especially for character recognition tasks such as reading zip codes, digits, and more.

There are four main operations in CNNs, including Convolution, Non-Linearity, Pooling or Sub Sampling, and Classification.

Applications of Convolutional Neural Networks

A convolutional neural network holds much promise for businesses. Its development and implementation provide an in-depth insight into visual content and how data impacts customer satisfaction.

Here are some key applications of CNNs.

Image Recognition: Image recognition is the capability of software to recognize people, places, objects, actions and content in images. Using CCNs help identify each content in the picture, find unique features, and compare all the data collected with existing data in the database to match the image.

Understanding Climate: CNNs can be leveraged to tackle climate change. They assist in understanding the reasons for drastic changes in the climate and how scientists could experiment in curbing the effect. The data collected by CNNs can also provide greater social and scientific insights.

Documents Analysis: A convolutional neural network is able to do document analysis, not just for handwriting analysis, but also has a major stake in recognizers. Research shows that the use of this neural network and other novel algorithms can minimize the error rate by a minimum of 0.4% at a character level.

Action Recognition: Building an action recognition system is a daunting task as it requires solving the translations and distortions of features in different patterns which belong to the same action class. Using a three-dimensional receptive field structure of the modified CNN model provides translation invariant feature extraction capability. The use of shared weight also lessens the number of parameters in the action recognition system.