What is Back Propagation

Back propagation is an algorithm that modifies the weights and biases of a neural network to reduce error and improve accuracy. The goal of back propagation is to minimize the difference between the network's output and the desired output. This is an iterative process that continues until the network can reliably produce the desired output.

Neural networks consist of layers of interconnected neurons, including an input layer, hidden layers, and an output layer. Forward propagation occurs when data is passed through the network from the input layer to the output layer. During forward propagation, each neuron calculates a weighted sum of its inputs and passes the result through an activation function.

• Weights define the strength of the connections between neurons.
• Activation functions introduce non-linearity, allowing the network to model complex relationships.
• Biases are additional parameters that shift the activation function and improve the network's flexibility.

The error, or the difference between the network's output and the desired output, is computed using a loss function. This error is then propagated back through the network. Back propagation uses this error signal to adjust the weights and biases of each neuron in the network, with the goal of reducing the error in future forward propagations. The process of adjusting the weights and biases is often done using gradient descent, which iteratively moves the weights and biases in the direction that reduces the error most quickly.

Back propagation is used to train many different types of neural networks, including static and recurrent networks. Static back propagation is used with feed-forward networks, where data flows in one direction from input to output. Examples of applications that use static back propagation include optical character recognition and spam detection. Recurrent back propagation is used with recurrent neural networks, which have loops and allow for more complex processing of sequential data. Recurrent neural networks are used for tasks like sentiment analysis and time series prediction.