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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 1

Introduction to Backpropagation

Video Summary

Backpropagation is an iterative 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 in a way that reduces the loss. Backpropagation is a powerful algorithm that has been used to train neural networks for a wide variety of tasks, including image classification, natural language processing, and speech recognition.

Knowledge Check

What is Backpropagation?

a mathematical optimization algorithm

a type of gradient descent

a type of reinforcement learning

a technique for training artificial neural networks

What is the goal of backpropagation?

to minimize the error between the predicted output and the actual output

to find the optimal weights for the artificial neural network

to find the optimal learning rate for the artificial neural network

to find the optimal activation function for the artificial neural network

What is the backward pass in backpropagation?

the process of calculating the gradients of the loss function with respect to the weights of the artificial neural network

the process of updating the weights of the artificial neural network

the process of computing the activations of the artificial neural network

the process of calculating the predictions of the artificial neural network

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Recurrent Neural Networks

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Backpropagation in Neural Networks