Question 6You train a ConvNet on a dataset with 100 different classes. You wonder if you can find a hidden unit which responds strongly to pictures of cats. (I.e., a neuron so that, of all the input/training images that strongly activate that neuron, the majority are cat pictures.) You are more likely to find this unit in layer 4 of the network than in layer 1.1 point

You train a ConvNet on a dataset with cats, dogs, birds, and other types of animals. You try to find a filter that strongly responds to horizontal edges. You are more likely to find this filter in layer 6 of the network than in layer 1. True/False?

Question 1Which of the following do you typically see in a ConvNet? (Check all that apply.)

Question 4You are working to create an object detection system, like the ones described in the lectures, to locate cats in a room. To have more data with which to train, you search on the internet and find a large number of cat photos.Which of the following is true about the system?1 point

Q.1 Which of the following are types of CNN architectures?1. RavNe2. AlexNet3. VGGNet4. JoNet

What statements are true?Group of answer choicesBy the end of our network, if we have multiple convolution and pooling layers, our final image is smaller than the original input image and we can classify the entire image, not just a region.Two common pooling methods are max pooling and mean pooling.If you use Photoshop to switch the location of the eyes and the mouth of a cat, a convolutional net with pooling layers would likely still "recognize" the image as a cat.Pooling introduces translation invariance by conveying what patterns are apparent in the image, without conveying where they are exactly.