Simplified AI 1.0 : Importance, Applications and Introduction to CNNs
Convolutional Neural Network is a class of deep feed forward neural networks largely inspired by the biological system, where the connectivity pattern between neurons depicts where each individual cortical neuron responds to stimuli only in the restricted area of the visual field known as the receptive field , i.e., restrictive sub-area of the input. The cortical neurons of various fields overlap so that they collectively represent the whole image.
In a Convolution Neural Network, each neuron processes data only for its receptive field and is arranged in such a way that they collectively represent the whole picture as well. In addition, both the biological visual system and the CNN have a layer structure, which slowly removes more and more functionality. Such layers are organized in-order of complexity, beginning with basic visual representations such as edges , lines, curves, etc., and increasingly more complex representations such as faces, instances, etc. This brings in the ability to grasp complex images.
CNNs are powerful image processing, artificial intelligence ( AI) that use profoundly deep learning to perform both generative and descriptive tasks, often using machine vision which includes image and video recognition, as well as recommendation systems and natural language processing (NLP).
Applications of CNNs
Detection of faces
Facial recognition is broken down into the following essential components by a convolutional neural network-
- Identify every face in the photo
- Focusing on each face despite outside factors like light, angle, pose, etc.
- Determining unique features
- Comparison of all the data obtained with existing data in the database to fit a face to a name.
- A similar method is often followed for the marking of scenes.
2. Analyzing documents
Convolutional neural networks can also be used for the study of documents. This is not only useful for analyzing handwriting but also has a major stake in recognizers. To be able to scan an individual's writing, a machine must execute nearly a million commands per minute, and then compare that to the wide database it has. With the use of CNNs and newer models and algorithms it is said that the error rate at a character level has been reduced to a minimum of 0.4 percent, although complete testing is yet to be seen widely.
3. Historic and environmental collection
CNNs are also used for more nuanced reasons, for example collections of natural history. Such collections serve as key players in recording significant historical parts such as biodiversity, evolution, loss of habitat, biological invasion and climate change.
4. Understanding climate
CNNs can be used to play a major role in combating climate change, especially in understanding why we see such dramatic changes and how we can experiment with curbing the effect. It is said that the data in these collections of natural history may also provide greater social and scientific perspectives, but this would require professional human resources such as researchers who would access these types of repositories physically. More manpower is needed to carry out deeper experiments in this field
5. Advertising
CNNs have already brought advertising into a world of difference by introducing programmatic purchasing and data-driven personalized advertising.
In the next blog, we’ll discuss miniscule aspects of CNNs and understand the Architecture of CNNs.
We, at IoTIoT, have a unique and strong understanding of the field of convolutional neural networks and data science. Our team of experienced professionals work with companies around the globe to better understand this space, as well as to carve out working solutions.
Also Know about exciting tech possibilities that can be added in your products from Innovators across the globe in Innovator Meetups
Join the Community : here
