How Neural Networks Perform Complex Tasks

Based in Aurora, Colorado, Muneeb Chawla is a tech entrepreneur with a background in areas such as machine learning, natural language processing, and statistics. In his work as a data scientist, Muneeb Chawla has also developed neural networks, which lie at the center of deep learning algorithms.

Also known as artificial neural networks (ANNs), these constructs have a structure that mimics the human brain in the way that neurons rapidly signal to each other within a complex environment.

Unlike the brain, ANNs are made up of node layers that have an input layer and a number of hidden layers, as well as an output layer. Every node connects to another node, with each possessing a specified weight and activation threshold. When a node surpasses the threshold value, it is activated and sends data to the network’s next layer.

Over an extended period, training data is used by neural networks to boost accuracy. Once the networks have been fine-tuned, they can be employed as AI tools that enable high velocity data to be clustered and classified. Practical uses of this include image and speech recognition. The search algorithm of Google is another example of a neural network that rapidly filters mass data and delivers relevant results.

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