Convolutional Neural Networks, or CNNs, extract information from images with the help of sequential pooling and convolutional layers. This deep-learning network feeds the extracted data into single or several connected layers for classification or prediction.
Although CNN architectures vary based on the job or issues they are meant to solve, a standard Convolutional Neural Network has these components:
- Input layer
- Convolutional layers
- Pooling layers
- Normalized layers
- Fully linked layers
- Dense layers
Applications of Convolutional Neural Networks in the AI Industry
1. Image Classification
In healthcare, retail, and security industries, CNN is used to classify images by identifying and recognizing objects or faces.
2. Object Surveillance
Widely used in autonomous vehicles, robots, and high-tech surveillance systems, convolutional neural networks detect objects in an image or video to track their movements over a particular period of time.
3. Image Segmentation
CNN breaks down images into individual components. For example, it can study medical images and identify different parts of the human body, which is why CNN is widely used in biotechnology and healthcare.
4. Natural Language Processing (NLP)
In Natural Language Processing, CNNs are used for text classification and sentiment analysis. CNN’s NLP ability makes it indispensable in advertising, e-commerce, and social media.
5. Style transfer
In artistic applications, CNN can transfer the style from one image to another. For instance, CNN can transform a painting into a photo format.
6. Speech Recognition
In applications that require speech recognition- especially in call centers, voice assistants, and the automotive industry, CNNs help convert speech to text and help recognize various accents.
7. Recommender Systems
In e-commerce, finance, and entertainment, CNNs analyze user data to make personalized customer recommendations.
