Mathematics for deep learning

Q1. A neuron with 3 inputs has the weight vector [0.2 -0.1 0.1]^T and a bias θ = 0. If the input vector is X = [0.2 0.4 0.2]^T then the total input to the neuron is:

0.02

0.2

-1

1

Q2. The most common Neural Networks consist of how many network layers?

3

2

4

1

Q3. An artificial neuron receives n inputs x1, x2, x3............xn with weights w1, w2, ..........wn attached to the input links. The weighted sum_________________ is computed to be passed on to a non-linear filter Φ called activation function to release the output.

Σ xi

Σ wi + Σ xi

Σ wi

Σ wi * Σ xi

Q4. Which of these is NOT the best example of Supervised Learning and Deep learning?

Image classification

Image learning

Sequence tagging

Text classification

Q5. Which of the following can be used for clustering of data ?

Radial basis function

Single layer perception

Self organizing map

Multilayer perception

Q6. Slots and facets are used in

Frames

Semnatic network

Rules

All of the above

Q7. In Delta Rule for error minimization

weights are adjusted w.r.to difference between desired output and actual output

weights are adjusted w.r.to change in the output

All of the above

weights are adjusted w.r.to difference between input and output

Q8. Back propagation is a learning technique that adjusts weights in the neural network by propagating weight changes.

Backward from sink to source

Backward from sink to hidden nodes

Forward from source to hidden nodes

Forward from source to sink

Q9. In propositional logic P ⇔ Q is equivalent to (Where ~ denotes NOT):

~ (P ˅ Q) ˄ ~ (Q ˅ P)

(P ˅ Q) ˄ (Q ˅ P)

(~ P ˅ Q) ˄ (~ Q ˅ P)

~ (P ˅ Q) → ~ (Q ˅ P)

Q10. Identify the following activation function : φ(V) = Z + (1/ 1 + exp (– x * V + Y) ), Z, X, Y are parameters

Ramp

Gaussian

Sigmoid

Step

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