Neural Networks – Short Explanation

Neurons are cells in the body that transmit information to other cells, including muscles. A neural network takes its name from these cells as it is loosely modeled on the human brain. Neural networks are computer algorithms designed to recognize patterns. They are useful tools to help us classify and cluster data. After they have been correctly trained, they can quickly group data that has not been categorized or labeled, helping save time.

Neural networks have fallen in and out of popularity in the AI field based on advances and changes in technology. Currently, neural networks are in a period of resurgence. This is primarily due to breakthroughs in deep learning as well as the improved graphic processing power of modern computer chips.

Understanding Neural Networks in the World of AI

Neural networks are a form of machine learning, where computer algorithms are trained and learn through the analysis of examples. These examples are manually categorized in advance with appropriate labels and fed in bulk to the neural network. The network itself is formatted in a manner where thousands of different processing nodes are interconnected. These nodes are then layered on top of each other, mimicking the human brain even more. Each node can communicate with the nodes it is connected to, similar to the synapses and neurons in a human brain.

The nodes are set up to “feed-forward” the data so that the data provided moves in only one direction. In this fashion, a single node could receive data from multiple nodes in one layer and then send that data onwards to numerous nodes in another layer.

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Neural networks learn by processing information. When a neural network is active, each node is given a “weight.” As data passes through multiple connections, the weight of each individual node is added together. Based on a specific threshold value that has been designated, the information will only be passed on if the weight surpasses the threshold value. To determine these weights, neural nets are initially created with entirely random values. As training data is fed into them, the weights and thresholds are adjusted until the output meets the objectives and expectations.