Artificial Intelligence is witnessing tremendous growth in this decade because it is bridging a gap between the capabilities of humans and computers.
Researchers and Scientists are innovating new techniques every day to make our lives easier by exploring various fields of AI.
An emerging area of research is computer vision.
One of its wonders is Convolutional Neural Networks, a Deep Learning algorithm perfected by advancements in the field of Computer Vision and Deep Learning.
Computer Vision endeavors to make machines view this world closer to the way we humans do and analyze it better. The machines then use this knowledge to further recognize and detect objects, classify images, or even recommend media content or designs for interiors.
Deep Learning, on the other hand, uses prior knowledge to come up with a qualitative decision but with a unique quality of learning from its own mistakes.
What are Convolutional Neural Networks (CNN)?
A Convolutional Neural Network (CNN) or ConvNet is a deep learning algorithm that can efficiently differentiate between two input images by assigning importance to various aspects or objects in the images.
With its lower pre-processing requirement in comparison to other classification algorithms, it is the most applied algorithm in analyzing visual imagery. Primitive classification algorithms use hand-engineered filters that humans train.
On the contrary, ConvNet being a deep learning algorithm is capable of learning these filters or characteristics on its own.
Thus, CNN’s have an added advantage of not depending on prior knowledge or training data. They are also free from human intervention in feature extraction.
You must be familiar with the connection of neurons in our brain. Taking inspiration by the organization of Visual Cortex, CNNs also attempts to mimic a similar connection pattern as that of neurons in the human brain.
We can implement CNNs in image and video recognition, image classification, image segmentation, recommender systems, natural language processing, and medical image analysis.
Why is Convolutional Neural Networks (CNN) important?
Convolutional Neural Networks or CNNs for deep learning are popular because of some factors that make it the simplest algorithm in terms of connectivity and complexity. Let’s discuss its importance:
- ConvNet eliminates the need for manual feature extraction and is independent of any prior knowledge or training data. Being a deep learning algorithm, CNNs learn the features and characteristics of an input image directly on their own.
- The receptive field is a restricted region of the visual field where individual cortical neurons respond to stimuli. Built with an architecture that’s analogous to the connectivity pattern in the human brain, CNNs are algorithms that produces the most accurate recognition results. Since receptive fields of different neurons partially overlap to cover the entire visual field, similarly, CNNs can extract the most prominent characteristics of an image with ease.
- The most significant features of CNNs are that you can retain them for new recognition tasks, making it easier for you to build on pre-existing networks.
How does Convolutional Neural Networks (CNN) work?
Now, you are familiar with the concept of CNNs but how do they work?
Well, the structure of Convolutional Neural Networks usually consists of 4 layers:
- Convolutional Layer
One of the building blocks of CNN, this layer discovers image features. It starts with the identification of general features like shapes and goes on detecting specific characteristics like elements of an object or recognizing a certain person’s face. It’s more of an application of a filter that results in some output which is image features.
- Rectified Linear Unit Layer
Rectified Linear Unit Layer aka ReLU is an extension of the convolutional layer. It’s mainly used to improve feature extraction which is achieved by increasing the image's non-linearity or in simpler terms removing extra fat from the image.
- Pooling Layer
This layer has the task of filtering the prominent features of an image. It minimizes the number of input parameters or regression. In simpler terms, this means focusing on important aspects of information obtained.
- Connected Layer
The very last stop in convolutional neural networks is the connected layer. It’s a standard feed-forward neural network where everything is already visible. Being the last straight line before the finish line, results are confirmed just after this layer.
Applications of Convolutional Neural Networks (CNN)
We can apply Convolutional Neural Networks in various fields. Natural Language Processing (NLP) and Computer Vision are the most popular technologies out of these. Let’s discuss:
- Natural Language Processing (NLP) refers to the ability of computers to understand a text or recognize speech as we do. Today, programmers deploy CNNs for sentence classification, topic categorization or even sentiment analysis. Moreover, programmers use CNNs for speech recognition tasks as they perform better than Deep Neural Networks. CNNs are also being implemented for text classification. Documents or sentences need to be classified according to a requirement. CNN is proven to be robust in doing so.
- In computer vision, CNNs are used to recognize the structure of an image. It has wide applications in technologies like facial recognition, image classification, action recognition, etc. In facial recognition, CNNs are deployed to recognize faces in an image or recognize their unique characteristics. In image classification, CNNs because of their learning ability provide better classification accuracy than other methods.
Companies like Tooliqa use deep learning methods to create sophisticated tools to improve accessibility and human experiences. They are also investing in CNNs to improve their accuracy and efficiency at the same time.
Today, technology is everywhere in the form of Artificial Intelligence. This AI processes huge amounts of data every minute to make a decision and make our lives easier.
Thus, CNNs paint a promising future where computers efficiently and accurately do visual tasks.
Having fewer problems when applied in Computer Vision and Natural Language Processing (NLP), CNNs offer better accuracy and efficiency in comparison to other methods or approaches.
The way CNNs reveal and describe human data is quite intelligible which enhances the belief that they are valuable for the future we are building.
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