Top 10 Convolutional Neural Network Questions Asked in FAANG Interviews

Get yourself ready with these top CNN questions that will help you crack FAANG interviews

Convolutional Neural Networks are a significant tool for most machine learning practitioners today. However, understanding these networks and learning to use them for the first time can be a challenge. In CNNs, convolution is the first layer to excerpt features from an input image. It is a mathematical operation, creating a set of weights and essentially making a representation of parts of the image. Convolution of an image with distinct filters can perform operations like edge detection, blur, and sharpening by applying filters.

Getting a job in this field is easy. Some technical and practical CNN questions will help you to get a job at FAANG. In this article we will take a look at the top 10 Convolutional Neural Network questions asked in FAANG interviews.

Why is CNN preferred over ANN for image data?

Feedforward neural networks can learn a single feature representation of the image but in the case of complex images, ANN will fail to give better predictions, this is because it cannot learn pixel dependencies present in the images. CNN can learn multiple layers of feature representations of an image by applying filters, or transformations

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What is the importance of the RELU activation function in CNN?

As a consequence, the usage of ReLU helps to prevent the exponential growth in the computation required to operate the neural network. If the CNN scales in size, the computational cost of adding extra ReLUs increases linearly

Explain the use of the pooling layer in CNN.

The main purpose of the pooling layer is to progressively reduce the spatial size of the input image, so that number of computations in the network is reduced. Pooling performs downsampling by reducing the size and sending only the important data to the next layers in CNN.

Explain the difference between valid padding and the same padding in CNN.

To sum up, 'valid' padding means no padding. The output size of the convolutional layer shrinks depending on the input size & kernel size. On the contrary, 'same' padding means using padding.

What is the use of the convolution layer in CNN?

A convolutional layer is the main building block of a CNN. It contains a set of filters (or kernels), and parameters, which are to be learned throughout the training. The size of the filters is usually smaller than the actual image. Each filter convolves with the image and creates an activation map.

How would you visualize features of CNN in an image classification task?

Convolutional neural networks, have internal structures that are designed to operate upon two-dimensional image data, and as such preserve the spatial relationships for what was learned by the model. Specifically, the two-dimensional filters learned by the model can be inspected and visualized to discover the types of features that the model will detect, and the activation map's output by convolutional layers can be inspected to understand exactly what features were detected for a given input image.

What do you understand by shared weights in CNN?

CNNs work by passing a filter over the image input. For the trivial example of a 4×4 image and a 2×2 filter with a stride size of 2, this would mean that the filter (which has four weights, one per pixel) is applied four times, making for 16 weights total.

Can CNN be used to perform Dimensionality Reduction? If yes, how?

Yes, CNN does perform dimensionality reduction. A pooling layer is used for this. The main objective of Pooling is to reduce the spatial dimensions of a CNN.

Define the term sparsity of connections in CNN

This implies that for each layer, each output value depends on a small number of inputs, instead of taking into account all the inputs.

List the hyperparameters of a pooling layer in CNN.

The hyperparameters for a pooling layer are Filter size, Stride, Max, or average pooling.