18 Questions
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
What is the purpose of back-propagation in neural network training?
To minimize the error between the predicted output and the actual output
How many iterations of training are required to update the weights and biases of the neural network?
At least one iteration
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.
What is the primary difference between a single layer and multiple layer perceptron?
The number of layers.
What is the formula for the sigmoid activation function?
$y = 1/(1 + e^{-x})$
What is the purpose of the error calculation in back-propagation?
To determine the loss and fine-tune the weights of the neural net.
What is the Leaky ReLU activation function?
$y = max(ax, x)$
What is the ultimate goal of back-propagation in neural network training?
To minimize the error rate and increase the generalization of the model.
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
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
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))
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
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
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
Test your understanding of deep learning concepts in natural language processing, including various types of activation functions and neural network architectures such as single layer and multiple layer perceptrons. Evaluate your knowledge of sigmoid, ReLU, tanh, and other functions, as well as their applications in NLP.
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