What is backpropagation?
- In machine learning, backpropagation is an effective algorithm used to train artificial neural networks, especially in feed-forward neural networks.
- Backpropagation is an iterative algorithm, that helps to minimize the cost function by determining which weights and biases should be adjusted. During every epoch, the model learns by adapting the weights and biases to minimize the loss by moving down toward the gradient of the error. Thus, it involves the two most popular optimization algorithms, such as gradient descent or stochastic gradient descent.
- Computing the gradient in the backpropagation algorithm helps to minimize the cost function and it can be implemented by using the mathematical rule called chain rule from calculus to navigate through complex layers of the neural network.
fig(a) A simple illustration of how the backpropagation works by adjustments of weights
Backpropagation in Neural Network
Machine learning models learn from data and make predictions. One of the fundamental concepts behind training these models is backpropagation. In this article, we will explore what backpropagation is, why it is crucial in machine learning, and how it works.
Table of Content
- What is backpropagation?
- Advantages of Using the Backpropagation Algorithm in Neural Networks
- Working of Backpropagation Algorithm
- Example of Backpropagation in Machine Learning
- Python program for backpropagation
A neural network is a network structure, by the presence of computing units(neurons) the neural network has gained the ability to compute the function. The neurons are connected with the help of edges, and it is said to have an assigned activation function and also contains the adjustable parameters. These adjustable parameters help the neural network to determine the function that needs to be computed by the network. In terms of activation function in neural networks, the higher the activation value is the greater the activation is.