Coursify | AI-Generated Courses

Create New Course Gallery Contact

Artificial Intelligence

Unit 1

Introduction to Optimization Gradient Descent Stochastic Gradient Descent Adam Optimization

Unit 2

Neural Networks

Introduction to Neural Networks Feedforward Neural Networks Convolutional Neural Networks Recurrent Neural Networks

Unit 3

Backpropagation

Introduction to Backpropagation Backpropagation in Neural Networks

Unit 3 • Chapter 2

Backpropagation in Neural Networks

Video Summary

Backpropagation is an algorithm used to train artificial neural networks. It works by calculating the gradient of the loss function with respect to the weights of the network, and then using this gradient to update the weights. This process is repeated until the network converges on a set of weights that minimizes the loss function. Backpropagation is a powerful algorithm that has made neural networks a powerful tool for machine learning.

Knowledge Check

Backpropagation is a type of what?

gradient descent

gradient boosting

neural network

regularization

What does backpropagation do?

calculates the gradient of the loss function with respect to the weights

calculates the gradient of the loss function with respect to the biases

calculates the gradient of the loss function with respect to the inputs

calculates the gradient of the loss function with respect to the outputs

What is the purpose of backpropagation?

to update the weights of a neural network

to update the biases of a neural network

to update the inputs of a neural network

to update the outputs of a neural network

Previous

Introduction to Backpropagation