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Artificial neural networks (ANNs) are powerful tools in the machine learning toolkit, used in a variety of ways to accomplish objectives such as image recognition, language processing, and game playing. Here's an overview about their fundamental component.

ANNs learn in a similar way to other machine learning algorithms: by finding relationships and patterns in training data. They are best suited to unstructured data where it’s difficult to understand how features relate to one another.

Neural networks consist of interconnected neurons that pass information by using electrical and chemical signals. Neurons pass information to other neurons and adjust information to accomplish a specific function.

When you grab a cup and take a sip of water, millions of neurons process the intention of what you want to do, the physical action to accomplish it, and the feedback to determine whether you were successful.

Simply: a neuron consists of dendrites that receive signals from other neurons; a cell body and nucleus that activates and adjusts the signal; an axon that passes the signal to other neurons; and synapses that carry, and in the process adjust, the signal before it is passed on.

Through approximately 90 billion neurons working together, our brains can function at the high level of intelligence that we know, and take for granted.

Like neurons, the Perceptron receives inputs (like dendrites), alters these inputs by using weights (like synapses), processes the weighted inputs (like the cell body and nucleus), and outputs a result (like axons).

The analogy of ants is a useful one here. Individual ants fulfil small tasks that are seemingly insignificant, but when the ants act as a colony, intelligent behavior emerges. Similarly, individual Perceptrons contribute to a greater goal in the ANN.

If you want to get into the details, learn more about artificial neural networks in Grokking AI Algorithms with Manning Publications:, consider following me - @RishalHurbans, or join my mailing list for infrequent knowledge drops:

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