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Neural network is the present and the future. The different types of neural networks like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and others have made huge improvements in the Deep Learning technology.
Neural Networks are in fact advance machine learning algorithms. The key to the efficiency of neural networks is they are very much adaptive and they can learn very quickly.
Neural networks are also known as Artificial Neural Networks (ANNs). They are a subset of machine learning and are at the heart of deep learning algorithms. The human brain inspires the development of neural network. They work by mimicking the way that biological neurons signal to one another.
Neural networks depend on their training data. Thus they can learn and improve their accuracy over time. Speech recognition or image recognition using Neural Networks can take only minutes versus hours when comparing with the manual identification by human experts. Google’s search algorithm is one of the famous neural networks.
There are many processors in Neural Network. These processors arrange as tiers. The first tier receives the unprocessed input similar to how the human optic nerve receives the raw information.
Every successive tier then accepts input from the tier before it. Then they pass its output to the tier after it. The last tier provides the final output.
Each tier has small nodes. The nodes are connecting to the nodes in the tier before and after. Also, each node in the neural network has its own type of knowledge. This including its programming rules and also the rules it has learnt by itself.
Neural networks are the main part of most of the deep learning applications. Therefore, from facial recognition and object detection, to building human-like chatbots, neural networks are essential.
Neural networking technology is the present and the future of Science. So you should start learning right now!
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Since neural networks offer various problem solving techniques, there is virtually no limit to the areas for its application.
Some common applications of neural networks today are image or pattern recognition, self-driving vehicles, facial recognition, data mining, email spam filtering, medical diagnosis, and cancer research.
There are many other ways that neural nets are using today, and the usage is increasing rapidly.