Posted on : 19 Apr, 2021, 06:25:12 PM

Top 50 Deep Learning Interview Questions And Answers

Top 50 Deep Learning Interview Questions And Answers


Deep learning is becoming one of the fastest-growing fields of the IT sector in the 21st century. It refers to well-covered skills and a set of technology that permits machines to predict outputs from layered input sets. Working with it required proper knowledge & efforts; deep learning skills are being embraced by organizations globally, which is prominently seen in the interviews. 


Interview questions can sometimes get a bit tougher to answer. That’s why Wissenhive presented this blog named ‘Top 50 Deep Learning Interview Questions’ while putting together the most asked interview questions with answers by industry experts and professionals.

 

1. What do you understand by Deep Learning?

Deep learning refers to a part of machine learning that involves a large volume of structured, semi-structured, or unstructured data while using complex algorithms inspired by the brain’s function and structure to train neural networks. Deep learning also performs various complex operations to extract hidden features and patterns.

 

2. Differentiate between Deep Learning and Machine Learning?

Scope Deep Learning Machine Learning
Dependency on data Performs exceptionally well on the large data set Great performance on data set which are small to medium-sized
Dependency on Hardware A high-end machine with top-notch configurations is required to assess data. Low and machine can suffice
Time of Execution It can take up to weeks as the process is long Ranges from few minutes to certain hours
Feature Engineering Understanding of best features to represent data is not required Understanding of features is needed to represent the data
Interpretability It is very difficult Some algorithms can be interpreted easily, while some are impossible.

 

3. Differentiate between Supervised Learning and Unsupervised Learning?

Scope Supervised Learning Unsupervised Learning
Deals with Labeled data Unlabeled data
Computational complexity High Low
Analyzation Offline Real-time
Accuracy Produces accurate results Generates moderates results
Sub-domains
  • Classification
  • Regression
  • Clustering
  • Association rule mining

 

4. What are the supervised learning algorithms?

There are three algorithms that include supervised learning.

  • Convolution neural network
  • Artificial neural network
  • Recurrent neural network

 

5. What are the unsupervised learning algorithms?

There are three algorithms that include supervised learning.

  • Boltzmann Machine (Deep belief networks)
  • Self Organizing Maps
  • Auto Encoders

 

6. What do you understand by Perceptron?

Perceptrons are very similar to the human brain’s neuron that receives and collects inputs from multiple entities and applies or provides inputs to those who transform them into output. It refers to an algorithm that is mainly used for performing supervised learning of binary classifiers. This algorithm is used in RNN, CNN, GAN, etc.

 

7. What is forward propagation?

Forward propagation refers to a process where inputs are passed to the hidden layer and weights. In every hidden layer, the activation function’s output is calculated until the other layer is processed. The process begins with the input layer and moves towards the final layer of output.

 

8. What do you understand by Backpropagation?

Backpropagation Learning

Backpropagation refers to one of the training algorithms that is used for multilayer networks. It works on transferring the error data or information from the network end to all inside network weights. It is divided into multiple steps, and those are

  • It can generate output by forwarding the propagation of training data through various networks.
  • It uses the targeted output value and value to compute derivation error by concerning activations of output.
  • It works on updating the weights.
  • It can backpropagate in computing error derivative by showing concern in activations output in the previous layer and continue all the hidden layers.

 

9. What are some of the most popular deep learning applications?

There is a various application included in deep learning, but some of the popular applications are:

  • Computer Vision
  • Sentiment Analysis
  • Object Detection
  • Machine translation
  • Automatic Text Generation
  • Image Recognition
  • Natural Language Processing

 

10. What are some of the tools and frameworks you have used in Deep learning?

The answer to this question based on an individual’s knowledge and skill level so, make sure you answer this question based on your experience, but some of the top frameworks of deep learning are

  • Keras
  • TensorFlow
  • CNTK
  • PyTorch
  • Caffe2
  • MXNet
  • Theano

 

11. Explain Boltzmann Machine?

The Boltzmann machine is one of the basic models of deep learning, which resembles a Multi-Layer Perceptron’s simplified version. This model features a hidden layer and a visible input layer which is the two-layer neural net that makes proper stochastic decisions to check whether a neuron should remain on or off. Nodes can connect across different layers, but two nodes that belong to the same layer can not connect.

Boltzmann Machine

 

12. What do you mean by the Restricted Boltzmann Machine?

An RBM or Restricted Boltzmann Machine refers to an undirected graphical model, which is a very popular algorithm in the deep learning field.

  • Regression
  • Classification
  • Collaborative filtering
  • Topic modeling
  • Dimensionality reduction

Restricted Boltzmann Machine

 

13. What do you understand by Overfitting?

Overfitting refers to a very common issue that occurred while working with deep learning. Overfitting refers to a scenario where the deep learning algorithms check data to gain some valid information. It makes the model of deep learning picking up noise rather than valuable and useful data, causing very low bias and high variance while making the model less accurate.

Underfitting and Overfitting in Machine Learning

 

13(a). What do you mean by Activation functions?

Activation functions are deep learning entities that are used in translating inputs into a usable parameter output. It decides if neurons should be activated or not by calculating the actual weighted sum with the bias while making the model output non-linear.

Activation Function

 

14. What are the various types of Activation functions?

There are various types of Activation function, and those are

  • ReLU
  • Sigmoid
  • Tanh
  • Softmax
  • Linear

15. What are the different stages of the model building?

There are few stages that are included in the building model, and those are

  • Deep understanding of business model
  • Data cleaning
  • Data acquisitions
  • Data analysis
  • Using machine learning algorithms in model making
  • Using an unknown dataset to check the model accuracy

 

16. How many layers are included in the neural network?

There are three different types of layers included in neural networks.

  • Input layer - It includes input neurons that send detail and information to the hidden layer.
  • Hidden layer - It sends data to the output layers
  • Output layer - It made data available at the output layer.

 

17. Why is the Fourier transform function used in Deep Learning?

Fourier transform function or the package used to analyze, manage and maintain the large number of data presented in the database to take real-time array data and process them. It maintains and ensures high efficiency and makes the model open to process on multiple signals.

 

18. What are some of the steps involved in training a perception?

There are mainly five steps involved in deep learning for training a perception, and those are 

  • Initialize weights and thresholds
  • Providing inputs
  • Calculating outputs
  • Updating weights
  • Repeating 2 to 4 steps again

 

19. What do you understand by the term Loss Function?

It is used as an accuracy measurement function to check if the neutral network has accurately learned the training data or still learning by comparing the training dataset to the testing dataset. It is a primary performance measure of the neural network.

Loss Function

20. What do you mean by Gradient Descent?

Gradient Descent'

Gradient Descent refers to one of the most used optimal algorithms to minimize errors and maximize the cost function. The main aim behind Gradient Descent is to find the local-global minimum of the function and determine in providing direction to reduce the model’s error.

 

21. What are the different variants included in gradient descent?

Three are three different types of variant included in gradient descent, and those are

  • Stochastic gradient descent 
  • Mini-batch gradient descent
  • Batch gradient descent

 

22. Differentiate between Batch and Stochastic Gradient Descent

Scope Batch Gradient Descent Stochastic Gradient Descent
Usage of dataset computes the gradient using the entire dataset. Computes the gradient using a single sample.
Converge It takes time to converge because of the huge data volume and slow updations of weights. Converges faster than the batch because stochastic is more frequent in updating weight.

 

23. What is Data Normalization?

Data normalization is a process of reforming and standardizing data which is the pre-processing step that eliminates redundancy in data, is used to rescale value to fit in a particular or specific range, and assures netter convergence in the backpropagation process.

 

24. Explain a computational graph in Deep learning?

A computation graph in deep learning refers to an operation series that helps take in the inputs and arrange them in the graph structure as nodes. Computation graphs can be considered as an implementation of mathematical calculations in a graph to help in high performance and parallel processes in terms of capability.

Computation Graph in Deep Learning

 

25. What do you understand by Cost Function?

The cost function is also referred to as ‘’error’’ or ‘’loss’’, which focuses on evaluating the model’s performance and computing the output layer error during backpropagation. It also pushes error backward through the strong neural network and uses them during other training functions.

 

26. What do you understand by the Swish function?

It is one of the self-gated activation functions that is designed and developed by Google. The mathematical formula of the swish function is

 

27. What do you mean by Autoencoders?

The Autoencoders refers to an artificial neural network that helps in learning without any supervision and direction. These networks have advanced ability to learn automatically through mapping the inputs in the corresponding outputs, including two entities. 

  • Encoder
  • Decoder

Autoencoders

 

28. What are the different types of Autoencoders?

There are four different types of autoencoders, and those are

  • Convolutional autoencoders
  • Contractive autoencoders
  • Deep autoencoders
  • Sparse autoencoders

 

29. Where can Autoencoders be used?

There are wide uses of autoencoders, but some of the popular uses are 

  • Adding color to colorless (black & white) images
  • Dimensionally reduction
  • Removes noises from images
  • Feature variation and removal

 

30. What are the steps included while using the gradient descent algorithms?

There are mainly five steps included while using the gradient descent algorithms.

  • Initializing weights and biases for the network
  • Sending input data through various network
  • Changing values to minimize the loss function in neurons 
  • Calculating the difference between predicted and expected values (the error)
  • Various iterations in determining the best weights for working efficiently

 

31. Differentiate single-layer and multi-layer Perceptron

Scope Single-layer perceptron Multi-layer perceptron
Classifies Cannot classify non-linear data Classify non-linear data
Parameter amount Limited amount of parameters Withdraws loads of parameters
Efficiency Less efficient Highly efficient

 

32. What do you understand by Binary Step Function?

It refers to an activation function that is usually threshold-based. If the value of the input is below or above a specific threshold limit, the neuron is activated, then it sends a similar signal to the other layer and does not allow outputs that are multi-value outputs.

 

33. What do you mean by the ReLU function?

The full form of ReLU is a rectified linear activation unit, and it refers to a unit or node that implements the activation function. Usually, most of the networks use the rectifier function for the hidden layers that are referred to as a ratified network. It is considered one of the few milestones in deep learning fields or revolution.

 

34. What do you understand by dropout?

Dropout in deep learning is a method that helps in avoiding overfitting a model. If the value of dropout is too low, it will leave a very minimal impact on learning. If the dropout value is too high, that means the model can under-learn, which causes lower efficiency.

 

35. What is the advantage of using Tensorflow?

There are numerous advantages of using Tensorflow, but some of the main benefits are

  • Platform independence
  • The high amount of flexibility
  • Large community
  • Open-source
  • Trains using GPU and CPU
  • Supporting auto differentiation and its features
  • Handles easily asynchronous and threads computation

 

36. What are the three different elements included in TensorFlow?

There are three different types of elements included in Tensorflow, and those are

  • Constants
  • Placeholders
  • Variables

 

37. What is the main work of Decision Trees?

  • Decision trees are easy to debug, easy to understand, and very flexible.
  • No transformation or preprocessing of required features
  • Prone to overfitting, but the user can use Random forests or pruning to avoid this situation.

 

38. What do you understand by the term an Array?

  • An Array refers to elements that are well indexed and makes easy access to specific elements.
  • Operations like deletion and insertion work faster in array
  • Arrays are of fixed size
  • Elements are consecutively stored in arrays
  • Assigned memory during compile time
  • Inefficient memory utilization in arrays

 

39. What are the advantages of an Array?

There are many advantages of using an Array, and those are 

  • Enabled random access
  • Cache friendly
  • Saves memory
  • Helps in code reusability
  • Predictable compile timing

 

40. What does the linked list mean?

  • Accessed elements in a cumulative manner
  • It makes operation a little slower and takes linear time.
  • It is flexible and dynamic.
  • Allocated memory during runtime or execution in the linked list
  • Efficient memory utilization in the linked list
  • Stores elements randomly in the linked list

 

41. What do you mean by CNN?

The full form of CNN is convolutional neural networks that are used in performing images and visuals analysis. These neural network classes input a multi-channel image and work on it.

 

42. What are the different types of layers presented in CNN?

There are mainly four different types of layers presented in convolutional neural networks.

  • Convolution
  • ReLu
  • Pooling
  • Connectedness

 

43. What do you understand by the term Pooling in Convolutional Neural Networks?

Pooling helps in reducing convolutional neural networks and spatial dimensions. It also advanced in performing operations like down-sampling to create a pooled feature map and reduce the dimensionality by sliding a filter matrix over the input matrix.

 

44. What do you understand with valid padding and the same padding?

  • Valid padding - Used when there is no need for padding. The dimensions of the output matrix (n – f + 1) X (n – f + 1) after convolution.
  • Same padding: adding a padding element around the output matrix and dimensions remain as the input matrix.

 

45. Why is mini-batch gradient so popular?

There are three reasons with makes mini-batch gradient popular, and those are

  • Comparatively more efficient to stochastic gradient descent.
  • Find the flat minima for Generalization.
  • Avoids local minima to allow gradient approximation for the entire dataset.

 

46. What are the applications used for transfer learning?

It is a scenario where the large models are trained on the dataset with huge data. This model is designed and used for simpler datasets that provide extremely efficient results and accurate neural networks. Some of the popular forms of transfer learning are

  • ResNet
  • GPT-2
  • BERT
  • VGG-16

 

47. How is the LSTM network, and how it works?

The full form of LSTM is Long-Short-Term Memory, one of the special kinds of recurrent neural networks capable of remembering information for a longer time and active in learning long-term dependencies as its default behavior. The three main steps of the LSTM network are

  • Decides what to remember and what to forget
  • Update the cell value selectively
  • Decides what current state part makes it to the output

 

48. What are the differences and similarities in bagging and boosting?

  Bagging Boosting
Similarities
  • Uses Voting
  • Combines modes of the same type
Differences
  • Individual models are built separately.
  • Equal weight is given to all models.
  • The performance of those built previously influences each new model.
  • Weights a model’s contribution by its performance

 

49. What do you understand by GANs?

The full form of GANs is Generative Adversarial Networks which is used for achieving deep learning’s generative modeling. It refers to an unsupervised task that includes pattern discovery in the data for generating output. GANs mostly used in activities like 

  • Image enhancement
  • Creation of art
  • Image translation

We, Wissenhive, hope you found the blog useful and helpful. The questions covered in this blog are the most sought-after interview questions for deep learning that will help the interviewee in acing their next interview!

If you are looking forward to learning and mastering all of the Deep learning or machine learning engineering skills & concepts and earning a certification in the same, do take a look at Wissenhive’s advanced and latest Deep learning-related certification offerings.

 

 

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