What is a Neural network How does it works in R

This recipe explains what is a Neural network How does it works in R

Recipe Objective - What is a Neural network. How does it works in R?.

Neural network is a circuit of neurons or in broader sense, an Artificial Neural Network which composes of nodes that are basis of deep learning inspired by human brain in the same way biologically created neurons signal each other, used in solving artificial intelligence problems.

Neural networks comprises of node layers, with an input layer, one or more hidden layers, and finally an output layer. Nde assigns a number to its incoming connection known as 'weight'. It also contain an associated threshold value. If the output of individual node is above the specified threshold value then that node is activated thereby sending data to the next layer of the network. No data is passed to the next layer of the network if the output of individual node is below the specified threshold value. Neural networks learn and improve their accuracy and confidence using training data. During the training of neural network, random values are assigned to all weights and thresholds of neural network.

Complete Guide to Tensorflow for Deep Learning with Python for Free

How does Neural Network works in R?

Neural network in R can be implemented using deep learning frameworks like Tensorflow and Keras and installing there packages in R.

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