What are Convolutional Neural Networks?
A convolutional neural network (CNN) is a deep learning algorithm used to take image, speech, or audio inputs and analyze or classify them. CNNs are a type of neural network, and they work, in simple terms, by using pattern recognition.
More technically, a CNN consists of three types of layers used to reduce source files into an easier-to-process form without dropping features that are essential to its ultimate prediction (I.e., determining if it’s analyzing a picture of a stop sign or a speed limit sign). The ‘convolution’ part refers to mathematical functions that use filters on the input data to produce a feature map, which is then used for classification.
Think of it this way—convolution is like taking a magnifying glass and looking at a picture in the very top corner and then slowly scanning the glass across the top part, then the middle part, then the bottom part to get the full picture.
Why are Convolutional Neural Networks So Important?
Real-world deployment of CNNs is increasingly rapidly, as these networks are some of the most effective at performing certain tasks the human brain can perform and classifying inputs based on what they “see.”
Practically speaking, CNNs allow programmers to cut out manual, time-consuming methods of identifying objects in images, speech, or audio inputs. To do this, they use three types of layers:
- Convolution Layer: Also known as the “kernel,” this is the core building block of a CNN. In this layer, a matrix scans the source to extract and pinpoint high-level features, such as edges and bright spots in images.
- Pooling Layer: The pooling layer’s core responsibility is to reduce the size of the source material and lessen the computational power required for data processing. It also recognizes dominant features, helping to further train the model. For example, the pooling layer is where the algorithm learns to differentiate a dog’s nose from a human’s nose.
- Fully Connected (FC) Layer: In the FC layer, the data is classified via flattening, or converting the data into a one-dimensional array, the pooling layer’s outputs. After this layer, the CNN can determine the probability of the input being within a certain class by recognizing larger and more complicated shapes.
As CNNs scan the image, they retain critical data from the previous layer and feed it forward to the next layer. First, the algorithm might recognize a border, then a color pattern, then a facial feature, and so on. If you’re telling a CNN to recognize a dog, it’s going to investigate every layer for a feature that resembles a dog and push relevant features forward while leaving behind the data that doesn’t support the search for a dog.
How Do Convolutional Neural Networks Actually Work?
Since computers can’t look at an image and process the contents instantly the way a human brain can, they use CNNs to first extract features and then classify them. To do so, they use a filter matrix and assign each square a numeric value the computer can read.
The CNN then uses these numbers to identify atypical elements in an image that don’t follow the pattern they’re looking for. CNNs can identify an image without having to first “learn” it using algorithms that relate characteristics from previous images to a new one. Thus, they’re more effective at identifying and sorting images and videos than most other algorithms.
Common CNN Industry Use Cases
The top application for convolutional neural networks is image recognition and classification, which can be applied across many industries in a variety of use cases. Some of these examples include the following.
Marketing
CNNs can be used to recognize, tag, and categorize images, which can help build recommender engines. This allows marketers to put more applicable products and ads in front of users based on a user’s previously expressed behavior and interests.
Healthcare
Healthcare has benefited hugely from CNNs, most notably with medical image computing that can detect anomalies with more precision than the human eye. When integrated with radiology technology, CNNs can help doctors identify cancerous tumors. Medical image computing also helps them predict interactions between molecules and associated proteins to aid in drug discovery and find potential treatments.
Automotive
CNNs are used to make self-driving cars safer and more accurate. CNNs can map pixels from the front-facing camera of a car and input them into steering commands. They can also use image recognition to detect lane lines and hazards in the road.
Getting the Most Out of CNNs
The advanced use cases for CNNs are increasing every day, indicating that their cross-industry impact will only grow in the coming years. Everyday examples, like handwritten character recognition, next word prediction, and auto-translation, are getting smarter and more accurate.
Are you curious to learn more about the impact that CNNs and other deep learning algorithms could have on your organization? Schedule a free AI assessment today to get personalized data science recommendations guaranteed to deliver meaningful results.
