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Convolutional Neural Networks/Week1/Week 1 Quiz - The basics of ConvNets.md

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+## Week 1 Quiz - The basics of ConvNets
+
+
+
+1. What do you think applying this filter to a grayscale image will do?
+
+  - Detect horizontal edges
+
+  - > Detect vertical edges
+
+  - Detect 45 degree edges
+
+  - Detect image contrast
+
+2. Suppose your input is a 300 by 300 color (RGB) image, and you are not using a convolutional network. If the first hidden layer has 100 neurons, each one fully connected to the input, how many parameters does this hidden layer have (including the bias parameters)?
+
+  - 9,000,001
+
+  - 9,000,100
+
+  - 27,000,001
+
+  - > 27,000,100
+
+3. Suppose your input is a 300 by 300 color (RGB) image, and you use a convolutional layer with 100 filters that are each 5x5. How many parameters does this hidden layer have (including the bias parameters)?
+
+  - 2501
+
+  - 2600
+
+  - 7500
+
+  - > 7600
+
+4. You have an input volume that is 63x63x16, and convolve it with 32 filters that are each 7x7, using a stride of 2 and no padding. What is the output volume?
+
+  16x16x32
+
+  29x29x16
+
+  > 29x29x32
+
+  16x16x16
+
+5. You have an input volume that is 15x15x8, and pad it using “pad=2.” What is the dimension of the resulting volume (after padding)?
+
+  19x19x12
+
+  17x17x10
+
+  > 19x19x8
+
+  17x17x8
+
+6. You have an input volume that is 63x63x16, and convolve it with 32 filters that are each 7x7, and stride of 1. You want to use a “same” convolution. What is the padding?
+
+  1
+
+  2
+
+  > 3
+
+  7
+
+7. You have an input volume that is 32x32x16, and apply max pooling with a stride of 2 and a filter size of 2. What is the output volume?
+
+  15x15x16
+
+  > 16x16x16
+
+  32x32x8
+
+  16x16x8
+
+8. Because pooling layers do not have parameters, they do not affect the backpropagation (derivatives) calculation.
+
+  True
+
+  > False
+
+9. In lecture we talked about “parameter sharing” as a benefit of using convolutional networks. Which of the following statements about parameter sharing in ConvNets are true? (Check all that apply.)
+
+  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.
+
+10. In lecture we talked about “sparsity of connections” as a benefit of using convolutional layers. What does this mean?
+
+  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.
+
+  Each layer in a convolutional network is connected only to two other layers
+
+  Regularization causes gradient descent to set many of the parameters to zero.

Convolutional Neural Networks/Week2/ResNets/Week 2 Quiz - Deep convolutional models.md

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+## Week 2 Quiz - Deep convolutional models
+
+1. Which of the following do you typically see as you move to deeper layers in a ConvNet?
+
+  nH and nW increases, while nC decreases
+
+  nH and nW decreases, while nC also decreases
+
+  nH and nW increases, while nC also increases
+
+  > nH and nW decrease, while nC increases
+
+2. Which of the following do you typically see in a ConvNet? (Check all that apply.)
+
+  > Multiple CONV layers followed by a POOL layer
+
+  Multiple POOL layers followed by a CONV layer
+
+  > FC layers in the last few layers
+
+  FC layers in the first few layers
+
+3. In order to be able to build very deep networks, we usually only use pooling layers to downsize the height/width of the activation volumes while convolutions are used with “valid” padding. Otherwise, we would downsize the input of the model too quickly.
+
+  True
+
+  > False
+
+4. Training a deeper network (for example, adding additional layers to the network) allows the network to fit more complex functions and thus almost always results in lower training error. For this question, assume we’re referring to “plain” networks.
+
+  True
+
+  > False
+
+5. The following equation captures the computation in a ResNet block. What goes into the two blanks above?
+```
+a[l+2]=g(W[l+2]g(W[l+1]a[l]+b[l+1])+bl+2+_______ )+_______
+```
+	> a[l] and 0, respectively
+
+	0 and z[l+1], respectively
+
+	z[l] and a[l], respectively
+
+	0 and a[l], respectively
+
+6. Which ones of the following statements on Residual Networks are true? (Check all that apply.)
+
+  > Using a skip-connection helps the gradient to backpropagate and thus helps you to train deeper networks
+
+  A ResNet with L layers would have on the order of L2 skip connections in total.
+
+  > The skip-connections compute a complex non-linear function of the input to pass to a deeper layer in the network.
+
+  The skip-connection makes it easy for the network to learn an identity mapping between the input and the output within the ResNet block.
+
+7. Suppose you have an input volume of dimension 64x64x16. How many parameters would a single 1x1 convolutional filter have (including the bias)?
+
+  2
+
+  4097
+
+  1
+
+  > 17
+
+8. Suppose you have an input volume of dimension nH x nW x nC. Which of the following statements you agree with? (Assume that “1x1 convolutional layer” below always uses a stride of 1 and no padding.)
+
+  > You can use a 1x1 convolutional layer to reduce nC but not nH, nW.
+
+  You can use a 1x1 convolutional layer to reduce nH, nW, and nC.
+
+  >  You can use a pooling layer to reduce nH, nW, but not nC.
+
+  You can use a pooling layer to reduce nH, nW, and nC.
+
+9. Which ones of the following statements on Inception Networks are true? (Check all that apply.)
+
+  > A single inception block allows the network to use a combination of 1x1, 3x3, 5x5 convolutions and pooling.
+
+  Making an inception network deeper (by stacking more inception blocks together) should not hurt training set performance.
+
+  > Inception blocks usually use 1x1 convolutions to reduce the input data volume’s size before applying 3x3 and 5x5 convolutions.
+
+  Inception networks incorporates a variety of network architectures (similar to dropout, which randomly chooses a network architecture on each step) and thus has a similar regularizing effect as dropout.
+
+10. Which of the following are common reasons for using open-source implementations of ConvNets (both the model and/or weights)? Check all that apply.
+
+  A model trained for one computer vision task can usually be used to perform data augmentation even for a different computer vision task.
+
+  > It is a convenient way to get working an implementation of a complex ConvNet architecture.
+
+  The same techniques for winning computer vision competitions, such as using multiple crops at test time, are widely used in practical deployments (or production system deployments) of ConvNets.
+
+  > Parameters trained for one computer vision task are often useful as pretraining for other computer vision tasks.