Backpropagation in Neural Networks: How it Helps?

Neural networks have shown significant advancements in recent years. From facial recognition tools in smartphone Face ID, to self driving cars, the applications of neural networks have influenced every industry. This subset of machine learning is comprised of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node is interconnected like human brain and has an associated weight and threshold. Suppose the output value of a node is higher than the specified threshold value, it implies that the node is activated and ready to relay data to the next layer of the neural network. There are various activation functions like Threshold function, Piecewise linear function or Sigmoid function.