What is a Back-propagation in network?

Back-propagation is a well-known algorithm. It was first launched in the 1970s, but it wasn’t until the 1980s that its full potential was discovered.

The back-propagation algorithm is still widely used for neural network training after more than 30 years.

Back-propagation is significant since it is both quick and efficient.

The backward propagation of defects inside a network is how back-propagation gets its name.

The training flow for back-propagation is identical to that illustrated in the Perceptron learning section.

  • The network receives an input vector, routed from the input layer to the hidden layer, and then to the output layer.
  • The expected result and the actual output for each output neuron in the network are used to calculate an error value.
  • Starting with the output neurons, the erroneous value is propagated backward via the network’s weights, through the hidden layer to the input layer.

The network is organized so that the hidden layer can recognize features. The output layer uses the hidden layer features to arrive at a solution.

The back-propagation process is not computationally expensive in modern computing, as you’ll see in the example implementation. Still, GPUs have made it possible to form enormous networks within clusters of GPU-based systems capable of astounding tasks like object recognition.