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.