Question

What is the difference between backpropagation and gradient descent?

Answer 1

Hint: Gradient descent is defined as the process of determining the first-order iterative optimization for establishing the local minimum of a differentiable function, whilst back-propagation is defined as the computation of derivatives.

Complete step-by-step solution:
The two neural networks, backpropagation and gradient descent, are similar in that they both take a dataset and try to find a hidden pattern in the data. They both use different approaches in their calculations from what is known as the error function. Back propagation is a technical approach to training artificial neural networks that can be used to solve problems. Gradient descent is one of the most popular methods in machine learning, which is based on a back propagation algorithm. Backpropagation has many benefits that make it advantageous over other techniques. It has good mathematical foundations and can easily be understood by linear algebra. Back propagation neural networks can also carry out complex calculations without any computational effort.
As the name implies, backpropagation is used to calculate how to reach the desired goal in a system and gradient descent is typically used to help find a solution for optimizing different parameters of the system. Gradient descent has been widely used by machine learning systems due to its ability to iterate over different parameters of the system, while back propagation increases in reliability as it converges with more data. Gradient descent is popularly known as an optimization algorithm that helps gradient-based methods approximate solutions efficiently.

Note: Backpropagation is a supervised learning algorithm that works by propagating errors from an output neuron to the input neurons and then to their corresponding weights so that the weights can modify their values. This algorithm generally trains noisy models, which may cause over-fitting issues.