convolutional neural network interview questions

Deep learning job interviews. A necessary evil. Most beginners in the industry break out in a cold sweat at the mere thought of a machine learning or a deep learning job interview. How do I prepare for my upcoming deep learning job interview? What kind of deep learning interview questions they are going to ask me? What questions should I ask them? These are just a few thoughts that run through the mind of any interviewee. The problem with most machine learning or deep learning interviews is that you never know whether you’ve to bring your lucky whiteboard marker or your lucky keyboard. Not to mention the deep learning questions that you will be asked in your next job interview are hardly predictable.

The good news? We’ve collated 100 deep learning technical interview questions from the insights of our industry experts on what kind of questions they ask most often. So, keep calm and read on to see what kind of questions you can expect in the hot seat in your next deep learning job interview. Ready to dive in? Then let’s get started!

Important Interview Questions On Convolution Neural Network- Deep Learning

Introduction

Computer Vision is evolving rapidly day-by-day. When we talk about Computer Vision, the term Convolutional Neural Networks (abbreviated as CNN) comes into our mind because CNN is heavily used here.

Therefore it becomes necessary for every aspiring Data Scientist and Machine Learning Engineer to have a good knowledge of these Neural Networks.

In this article, we will discuss the most important questions on the Convolutional Neural Networks(CNNs) which is helpful to get you a clear understanding of the techniques, and also for Data Science Interviews, which covers its very fundamental level to complex concepts.

20 Questions to Test your Skills on CNN (Convolutional Neural Networks)

A Convolutional neural network (CNN, or ConvNet) is another type of neural network that can be used to enable machines to visualize things.

CNN’s are used to perform analysis on s and visuals. These classes of neural networks can input a multi-channel and work on it easily with minimal preprocessing required.

These neural networks are widely used in:

Therefore, CNN takes an as an input, processes it, and classifies it under certain categories.

Source: Google s

Why do we prefer Convolutional Neural networks (CNN) over Artificial Neural networks (ANN) for image data as input?

1. Feedforward neural networks can learn a single feature representation of the but in the case of complex s, ANN will fail to give better predictions, this is because it cannot learn pixel dependencies present in the s.

2. CNN can learn multiple layers of feature representations of an by applying filters, or transformations.

3. In CNN, the number of parameters for the network to learn is significantly lower than the multilayer neural networks since the number of units in the network decreases, therefore reducing the chance of overfitting.

4. Also, CNN considers the context information in the small neighborhood and due to this feature, these are very important to achieve a better prediction in data like s. Since digital s are a bunch of pixels with high values, it makes sense to use CNN to analyze them. CNN decreases their values, which is better for the training phase with less computational power and less information loss.

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1. Given that there are so many deep learning algorithms, how will you determine which deep learning algorithm has to be used for a dataset.

Artificial Neural Network Artificial Neural Network or sometimes called Classic Neural Network is a connection of multilayered perceptrons. This algorithm can be used when the data is properly structured in a tabular form. Both Classification and regression problems can be solved using ANNs Convolutional Neural Networks These networks are the best proven ones to build any prediction model involving data as input. To put it in general terms, CNN works best on data with spatial relationships and hence these can also produce state-of-the-art results for NLP problems such as topic modelling, document classification and so on. Recurrent Neural Networks RNNs come into picture when we have sequential data where the order of the data entered is also important. RNNs can provide solutions for problems involving Time Series data. More often, rather than vanilla RNNs, gated networks like LSTMs (Long short term memory) and GRUs(Gated Recurrent units) are proven to give much better results. Autoencoders Autoencoders are widely used in the deep learning community these days because of its ability to operate automatically based on its inputs even before taking an activation function and final output decoding. These can be used when we have problems such as feature detection, recommendation systems and other compelling problems.

2. How do one-hot encoding and label encoding affect the dimensionality of a dataset?

Label encoding does not really affect the dataset in any way because in label encoding, we only provide labels to each category in the column.

In the above example, we are mapping Delhi -> 0, Hyderabad -> 1, and Chennai -> 2.

In one hot encoding, we create columns to each of the category in the dataset. Thus, the more the number of categories in the column, the more are the columns generated after one hot encoding. Let us consider the very same dataset that we saw above. After one hot encoding it will look like the table shown below

If the value is ‘Delhi’, then only the column meant for ‘Delhi’ takes the value 1 and the other columns takes the value 0.

Often, we dont consider the last/first category after one hot encoding the variable because it can be clearly understood that if all the existing entries for the category are 0, then it belongs to the category that we dropped. This is much clearly explained with the example below

Here , we already know that there are 3 unique categories in the variable (Delhi, Hyderabad, and Chennai). There are two zeros in the 3rd row which clearly implies that it does not belong to both the categories and the one which remains in Chennai. Therefore, the decoded value for that row is Chennai.

3. Why are GPUs important for implementing deep learning models?

Whenever we are trying to build any neural network model, the model training phase is the most resource-consuming job. Each iteration of model training comprises thousands (or even more) of matrix multiplication operations taking place. If there are less than around 1 lakh parameters in a neural network model, then it would not take more than a few minutes (or few hours at most) to train. But when we have millions of parameters, that is when our sizable computers would probably give up. This is where GPUs come into the picture. GPUs (Graphics Processing Units) are nothing but CPUs but with more ALUs (Arithmetic logic units) than our normal CPUs which are specifically meant for this kind of heavy mathematical computation.

4. Which is the best algorithm for face detection ?

There are several machine learning algorithms available for face detection but the best ones are the ones which involve CNNs and deep learning. Some notable algorithms for face detection are listed below FaceNet Probablisit Face Embedding ArcFace Cosface Spherface

5. What evaluation approaches do you use to gauge the effectiveness of deep learning models?

6. When training a neural network, you observe that the loss does not decrease in the first few epochs. What are the possible reasons for this?

7. What are the commonly used techniques to deal with the overfitting of a deep learning model?

8. What kind of gradient descent variant is the best for handling data that is too big to handle in RAM simultaneously?

9. How will you explain the success and recent rise in demand for deep learning in the industry?

10. How do you select the depth of a neural network?

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