Backpropagation

Definition of Backpropagation

Backpropagation is a neural network algorithm used to train artificial neural networks. The algorithm calculates the gradient descent of the cost function with respect to the weights of the neurons in the network. This allows the network to adjust its weights so that it can more accurately predict the desired outputs.

What is Backpropagation used for?

Backpropagation is a powerful and widely used algorithm in the field of machine learning and deep learning. It is a method of training neural networks by adjusting the weights of the neurons in order to minimize a cost function. This cost function helps to measure how far the output of the neural network is from the desired result. By using backpropagation, we can update weights so that they help us to reduce this error and approach an optimal solution. In other words, it helps to adjust the weights in order to minimize errors, resulting in a more accurate prediction or classification.

In addition, backpropagation is also known as an “error-correction” algorithm because it looks at each output neuron’s predicted value and compares it with its actual value. It then calculates how much each neuron should be adjusted in order to bring its predicted value closer to its actual value. The adjustments are made through a series of backward passes that propagate across all of the hidden layers until it reaches the input layer, thus allowing us to calculate derivatives for each parameter or weight. Once all of these derivative values have been obtained, we can use them as our guide for updating parameters during gradient descent optimization. Ultimately, this process allows us to improve our model’s performance by accurately measuring how far off our predictions were, and using that information to make adjustments that will better our results.