Tensorflow 2

Q1. Wherein neural internet architecture, does weight sharing occur in TensorFlow?

Recurrent Neural community

Convolutional neural community

b and c

Fully related Neural community

Q2. Y = ax^2 + bx + c (polynomial equation of degree 2)Can this equation be represented via a neural network of a single hidden layer with linear threshold?

Yes

Can't be said

No

I don't know

Q3. Can a neural network model the characteristic (y=1/x) in TensorFlow?

Yes

No

I don't know

Can't be said

Q4. Identify the lifeless unit in a neural community?

None of the above

A unit which does now not reply absolutely to any of the schooling styles

A unit which doesn’t replace throughout training by means of any of its neighbour

The unit which produces the most important sum-squared mistakes

Q5. What if we use a gaining knowledge of fee that’s too huge?

Network will converge

Network will not converge

Can’t Say

None of the above

Q6. Which of the subsequent declaration(s) effectively represents an actual neuron in TensorFlow?

A neuron has a single input, however, more than one outputs

A neuron has multiple inputs but a single output only

A neuron has a single enter and a single output best

All of the above statements are valid

Q7. Which of the following techniques perform comparable operations as the dropout in a neural community in TensorFlow?

Boosting

None of the above

Stacking

Bagging

Q8. A numeric variable can shop numeric values with a maximum of eight digits.

No

I don't know

Yes

Can't be said

Q9. In a neural network, which of the subsequent strategies is used to deal with overfitting in TensorFlow?

Dropout

Batch normalization

All of the above

Regularization

Q10. Which of the following functions shouldn't be used at the output layer to classify an image?

sigmoid

None of these

tanh

ReLU

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