Deep Learning in NLP: Activation Functions and Neural Network Architectures

Choose a study mode

Play Quiz

Study Flashcards

Spaced Repetition

Chat to Lesson

Podcast

Play an AI-generated podcast conversation about this lesson

Download our mobile app to listen on the go

Get App

Questions and Answers

What is the output of the neural network for the given input tuple X = (1,0,1)?

0.332

What is the error calculated in the first iteration of training?

0.1

What is the purpose of the learning rate in back-propagation?

To control the step size of each update of the weights and biases

What is the sigmoid function used for in the neural network?

To introduce non-linearity in the hidden layer Signup and view all the answers

What is the purpose of back-propagation in neural network training?

To minimize the error between the predicted output and the actual output Signup and view all the answers

How many iterations of training are required to update the weights and biases of the neural network?

At least one iteration Signup and view all the answers

What is the purpose of back-propagation in neural network training?

To fine-tune the weights of a neural net based on the error rate obtained in the previous epoch. Signup and view all the answers

What is the primary difference between a single layer and multiple layer perceptron?

The number of layers. Signup and view all the answers

What is the formula for the sigmoid activation function?

$y = 1/(1 + e^{-x})$ Signup and view all the answers

What is the purpose of the error calculation in back-propagation?

To determine the loss and fine-tune the weights of the neural net. Signup and view all the answers

What is the Leaky ReLU activation function?

$y = max(ax, x)$ Signup and view all the answers

What is the ultimate goal of back-propagation in neural network training?

To minimize the error rate and increase the generalization of the model. Signup and view all the answers

What is the purpose of the back-propagation algorithm in a neural network?

To compute the error gradients and update the weights and biases to minimize the error during training Signup and view all the answers

What is the type of neural network depicted in the figure, and what is its characteristic?

A multilayer feed-forward neural network, and its characteristic is that the information flows only in one direction, from input layer to output layer, without any feedback loops Signup and view all the answers

What is the activation function used in the provided example, and what is its formula?

The sigmoid function, and its formula is y = 1 / (1 + e^(-x)) Signup and view all the answers

How is the error calculated at each node in the neural network?

The error is calculated as the difference between the predicted output and the actual output, and then propagated backwards to update the weights and biases Signup and view all the answers

What is the learning rate in the provided example, and what is its role in the training process?

The learning rate is 0.9, and its role is to control the step size of each weight update during the training process Signup and view all the answers

What is the purpose of the bias term in the neural network, and how is it updated during training?

The bias term is used to shift the activation function, and it is updated during training using the error gradients and the learning rate Signup and view all the answers

Flashcards are hidden until you start studying