CNN EXAM WITH CORRECT QUESTIONS AND ANSWERS
| 2026 LATEST UPLOAD |GRADED A+
Convolutional neural networks (CNNs) - CORRECT ANSWER- In machine learning, a
convolutional neural network (CNN, or Convent) is a class of deep, feed-forward
artificial neural network that have successfully been applied to analyzing visual imagery.
It's the first layer to extract features from an input imp.
CNN image classifications - CORRECT ANSWER- Take an input image, process it and
classify it under certain categories. Computer sees an input image as array of pixels
and it depends on the image resolution. Based on the image resolution, it will see h x w
x d (h = Height, w = Width, d = Dimension).
RGB (3 CHANNELS) - CORRECT ANSWER- An image of 6 x 6 x 3 array of matrix
Grayscale - CORRECT ANSWER- An image of 4 x 4 x 1 array of matrix
CNN - CORRECT ANSWER- Each input image will pass it through a series of
convolution layers with filters (Kernels), Pooling, fully connected layers (FC) and apply
Soft ax function to classify an object with probabilistic values between 0 and 1.
Neural network with many convolutional layers - CORRECT ANSWER- a) input
b) feature learning:
- convoy + rely
- peopling
- convoy + rely
- pooling
c) classification
- flatten
- fully connected
-soft ax
Pooling - CORRECT ANSWER- Pooling layers’ section would reduce number of
parameters wen imp is too large.
Spatial pooling (subsampling or down sampling) - CORRECT ANSWER- Reduces the
dimensionality of each map but retains important info.
Spatial pooling types - CORRECT ANSWER- Max pooling, average pooling, sum
pooling
Max pooling - CORRECT ANSWER- Takes the largest element from the rectified
feature map.
| 2026 LATEST UPLOAD |GRADED A+
Convolutional neural networks (CNNs) - CORRECT ANSWER- In machine learning, a
convolutional neural network (CNN, or Convent) is a class of deep, feed-forward
artificial neural network that have successfully been applied to analyzing visual imagery.
It's the first layer to extract features from an input imp.
CNN image classifications - CORRECT ANSWER- Take an input image, process it and
classify it under certain categories. Computer sees an input image as array of pixels
and it depends on the image resolution. Based on the image resolution, it will see h x w
x d (h = Height, w = Width, d = Dimension).
RGB (3 CHANNELS) - CORRECT ANSWER- An image of 6 x 6 x 3 array of matrix
Grayscale - CORRECT ANSWER- An image of 4 x 4 x 1 array of matrix
CNN - CORRECT ANSWER- Each input image will pass it through a series of
convolution layers with filters (Kernels), Pooling, fully connected layers (FC) and apply
Soft ax function to classify an object with probabilistic values between 0 and 1.
Neural network with many convolutional layers - CORRECT ANSWER- a) input
b) feature learning:
- convoy + rely
- peopling
- convoy + rely
- pooling
c) classification
- flatten
- fully connected
-soft ax
Pooling - CORRECT ANSWER- Pooling layers’ section would reduce number of
parameters wen imp is too large.
Spatial pooling (subsampling or down sampling) - CORRECT ANSWER- Reduces the
dimensionality of each map but retains important info.
Spatial pooling types - CORRECT ANSWER- Max pooling, average pooling, sum
pooling
Max pooling - CORRECT ANSWER- Takes the largest element from the rectified
feature map.
