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computing systems inspired by the biological neural networks, where they learn progressively to do tasks by considering examples, generally without task-specific programming.
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For example, in image recognition, they might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat" and using the results to identify cats in other images.
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An (artificial) neural network is a network of simple elements called neurons, which receive input, change their internal state (activation) according to that input, and produce output depending on the input and activation. The network forms by connecting the output of certain neurons to the input of other neurons forming a directed, weighted graph, The weights as well as the functions that compute the activation can be modified by a process called learning which is governed by a learning rule.
- allows google to build and train neural nets up to five times faster than their first-generation system,
- tensorflow is a numerical computation library
- tensors are multidimensional data arrays