#neutralnetwork

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Neutral Network

Neural networks, also called artificial neural networks (ANNs), are a machine learning subset intermediary to deep learning algorithms.

As their name suggests, they mock how the human brain learns. The brain gets encouragement from the external atmosphere, processes the information, and also provides a product. As the task becomes more delicate, many neurons form a complex network that communicates with one another.

Activation Function in Neural Network

What is activation function in neural network ? Lets See 

An activation function is utilized to introduce non-linearity in an artificial neural network. It allows us to model a class tag or score that varies non-linearly with self-dependent variables. Non-linearity means that the product can not be replicated from a linear combination of inputs; this allows the model to learn complex mappings from the available data, and therefore the network becomes a universal approximator. On the other hand, a model which uses a direct function ( i.e. no activation function) is incapable to frame a sense of complicated data, similar to, speech, tape recordings, etc., and is efficient only over to a single level.

The activation function is the most important factor in a neural network which determines whether or not a neuron will be actuated or not and transferred to the coming level. This simply means that it'll decide whether the neuron’s input to the network is applicable or not in the process of the forecast. For this reason, it's also related to a threshold or changeover for the neurons which can meet the network.

Significance of Activation Function

An important characteristic of linear functions is that the composition of two linear functions is similarly a direct function. This means that indeed in veritably deep neural networks if we only had linear conversions of our data values during a forward pass, the learned mapping in our network from input to a product would also be direct.

Generally, the kinds of mappings that we're directing to learn with our deep neural networks are more complicated than simple linear mappings.

This is where activation functions come in. maximum activation functions are non-linear, and they've been taken in this way on purpose. Owning non-linear activation functions allows our neural networks to calculate arbitrarily complex functions.

Conclusion