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An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information.
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The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems.
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ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones. This is true of ANNs as well.
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Adaptive learning: An ability to learn how to do tasks based on the data given for training or initial experience.
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Self-Organisation: An ANN can create its own organisation or representation of the information it receives during learning time.
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Real Time Operation: ANN computations may be carried out in parallel, and special hardware devices are being designed and manufactured which take advantage of this capability.
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Activation Function
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Each neuron has an activation function that defines the output of the neuron
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The activation function is used to introduce non-linearity in the modeling capabilities of the network
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forwardpropagation
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occurs when the network is exposed to the training data and these cross the entire neural network for their predictions (labels) to be calculated.
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loss function
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to estimate the loss (or error) and to compare and measure how good/bad our prediction result was in relation to the correct result (remember that we are in a supervised learning environment and we have the label that tells us the expected value). Ideally, we want our cost to be zero
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as the model is being trained, the weights of the interconnections of the neurons will gradually be adjusted until good predictions are obtained.
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backpropagation
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Once the loss has been calculated, this information is propagated backwards.
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