The basics of ConvNets

The basics of ConvNets >> Convolutional Neural Networks

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Question 1

What do you think applying this filter to a grayscale image will do?

1 point

Detect horizontal edges

Detect vertical edges

Detect image contrast

Detect 45 degree edges

1 point

9,000,001

9,000,100

27,000,001

27,000,100

1 point

2501

2600

7500

7600

1 point

29x29x16

16x16x32

16x16x16

29x29x32

1 point

17x17x10

19x19x12

19x19x8

17x17x8

1 point

15x15x16

32x32x8

16x16x8

16x16x16

1 point

True

False

1 point

It allows parameters learned for one task to be shared even for a different task (transfer learning).

It reduces the total number of parameters, thus reducing overfitting.

It allows gradient descent to set many of the parameters to zero, thus making the connections sparse.

It allows a feature detector to be used in multiple locations throughout the whole input image/input volume.

1 point

Each layer in a convolutional network is connected only to two other layers

Each filter is connected to every channel in the previous layer.

Each activation in the next layer depends on only a small number of activations from the previous layer.

Regularization causes gradient descent to set many of the parameters to zero.

The basics of ConvNets >> Convolutional Neural Networks

The basics of ConvNets

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