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Neural networks are a type of artificial intelligence that is modeled on the way our brains work. It is a system of interconnected layers of nodes, or artificial neurons, which act as an information processing system. Neural networks use input data to learn, adjust, and develop their own behavior. They are used in a wide variety of applications, ranging from image and speech recognition, to robotics and control systems.
There are many types of neural networks, each with unique features and capabilities. The two most popular types are feed-forward networks and recurrent networks.
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| Feed Forward NN |
Feed-forward networks are the simplest type of neural network. They consist of input nodes, invisible layers of nodes, and output nodes. The nodes are connected in a series of links or weights, and each node receives inputs from all of the previous nodes and then produces an output. This output is sent to the next layer of nodes, and so on until the final output is produced. This type of network is used for problems such as image classification and pattern recognition.
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| Recurrent NN |
Other types of neural networks include self-organizing maps (SOMs), convolutional neural networks (CNNs), and deep belief networks (DBNs). SOMs are used for clustering and data compression; CNNs are used for image recognition; and DBNs are used for unsupervised learning.
No matter what type of neural network you’re using, they all rely on the same basic principles. Each type has its own strengths and weaknesses, and it’s important to understand how they work before deploying them in any practical application.
