Backpropagation algorithm

1. Backpropagation is an algorithm used in the training of feedforward neural networks for supervised learning. 

2. Backpropagation efficiently computes the gradient of the loss function with respect to the weights of the network for a single input-output example. 

3. This makes it feasible to use gradient methods for training multi-layer networks, updating weights to minimize loss, we use gradient descent or variants such as stochastic gradient descent. 

4. The backpropagation algorithm works by computing the gradient of the loss function with respect to each weight by the chain rule, iterating backwards one layer at a time from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this is an example of dynamic programming. 

5. The term backpropagation refers only to the algorithm for computing the gradient, but it is often used loosely to refer to the entire learning algorithm.

6. Backpropagation generalizes the gradient computation in the delta rule and is in turn generalized by automatic differentiation, where backpropagation is a special case of reverse accumulation (reverse mode)

Effect of tuning parameters of the backpropagation neural network :

1. Momentum factor :
2. Learning coefficient :
3.  Sigmoidal gain : 
4. Threshold value :