Backpropagation is an algorithm commonly used to train neural networks. Like other weak methods, it is simple to implement, faster than many other "general" approaches. Stay tuned with BYJU’S to learn more about other concepts such as continuity and differentiability. the target value yyy is not a vector. School University of Delhi; Course Title COMPUTER 303; Type. Back-propagation is defined as the process of calculating the derivatives whereas gradient descent is defined as the process of determining the first-order iterative optimization for determining the local minimum of a differentiable function. It is considered an efficient algorithm, and modern implementations take advantage of specialized GPUs to further improve performance. Sign up, Existing user? Partial computations of the gradient from one layer are reused in the computation of the gradient for the previous layer. But at those points you should still be able to understand the main conclusions, even if you don't follow all the reasoning. Essentially, backpropagation is an algorithm used to calculate derivatives quickly. The following code example is for a sigmoidal neural network as described in the previous subsection. X    In this neuron, we have data in the form of z=W*x + b, so it is a straight linear equation as you can see in figure 1. Observe the following equation for the error term δjk\delta_j^kδjk​ in layer 1≤k