Deep Learning Interview Questions And Answers

April 21st, 2024

Step on to read the top deep learning interview questions and answers to get into the company of your dreams!

Deep Learning Interview Questions And Answers

With the rise in Artificial Intelligence, Deep Learning (DL) has experienced growth too. Over the course of years, more businesses have begun to apply it in their working. The demand for these professionals is increasing and it's the right time to step into this field. With this deep learning interview questions and answers, acing an interview becomes easy.

This blog covers basic deep learning interview questions for freshers, intermediates and experienced professionals. Additionally, learn about the job trends and job opportunities in deep learning.

Basic Deep Learning Interview Questions for Freshers

Freshers find it a little more difficult to get selected due to the lack of experience. However, these questions are sure to lend a helping hand.

Question 1. Give some applications of deep learning.

Some of its applications are-

  • Automated translation
  • Classification & detection of objects
  • Automated text generation
  • Pattern recognition & NLP
  • Handwriting generation by machine
  • System to answer questions
  • Colorization of white & white images

Question 2. Explain a deep neural network?

A deep neural network (DNN) is basically an artificial neural network (ANN) with many layers within the output and the input layers. They use deep architectures and have various units and layers in a single layer.

deep neural network

Question 3. What is end-to-end learning in DL?

End-to-end learning in DL refers to a procedure where the model is fed with plenty of raw data. This complete data is trained together to rule out the desired result without any intermediate steps. This procedure is all about training the steps simultaneously and not sequentially.

Explore our Deep Learning training program to scale up your knowledge.

Question 4. What is data normalization?

Data normalization is a DL technique. In this, the data is transformed in such a manner that either has a similar distribution or is dimensionless. It is also referred to as feature scaling or standardization. This preprocessing procedure is to remove any redundant data from the data sets.

Question 5. Which techniques are used to achieve data normalization?

Data normalization is achieved through these techniques -

  • Rescaling
  • Z-score normalization
  • Mean normalization

Question 6. What is meant by neural networks?

Neural networks are artificial systems which are highly akin to the biological neural networks running through the entire human body. This set of algorithms aim at recognizing the underlying relationships in a given data set via methods similar to a human brain. These are the basis of deep learning.

neural networks

Question 7. Explain hyperparameters in DL.

hyperparameters in DL

Hyperparameters are variables that influence and determine how the network is trained and the network topology. These are set prior to training the model. Learning rate and number of hidden layers are a couple of these variables.

You may also read- Deep Learning With TensorFlow- A Complete Guide

Intermediate Deep Learning Interview Questions

As someone with a few years of experience, these intermediate deep learning interview questions are sure to help.

Question 8. Give a brief on the different types of DNNs.

There are seven types of DNNs.

  1. FeedForward Neural Network - It's the most neural network. The flow control here begins at the input layer and moves on to the output layer. 
  2. Radial Basis Function Neural Network - It usually has more than a layer and are generally used in power restoration systems.
  3. Multi-Layer Perceptrons (MLP) - It's a kind of feedforward ANN. This simple DNN consists of a line of truly linked layers.
  4. Convolutional Neural Network (CNN) - It is generally employed in computer vision.
  5. Recurrent Neural Network (RNN) - It was curated to solve the issue of sequential input data time-series. 
  6. Modular Neural Network - It's not a single network but consists of various tiny neural networks.
  7. Sequence to Sequence Model - Mostly, it's made of two RNN networks.

Question 9. What is forward and back propagation in DL?

Forward propagation in deep learning refers to the process wherein the neural network is trained via passing the input data through the network. Back propagation is the next step wherein the biases and weights are updated on the basis of the errors of the network's output.

Question 10. How is a multi-label classification problem different from a multi-class classification problem?

Intermediate Deep Learning Interview Questions

A multi-label classification problem is one where every label has a distinct classification task, which are related to one another in some way. A multi-class classification problem comprises two mutually exclusive classes.

Question 11. How is transfer learning advantageous?

Key advantages of transfer learning include-

  • Higher learning rate
  • Better initial mode
  • More accuracy after training

Question 12. What is tensor in reference to DL?

Tensor in DL is a multidimensional array. It represents a generalization of matrices and vectors. These key data structures are showcased as n-dimensional arrays of the base data types. In Tensor, the data type of every element is the same and is always known.

Question 13. What is batch gradient descent?

Batch gradient descent pertains to computation over the complete training set at every single step. Consequently, it is super slow when it comes to gigantic training sets. This also makes it computationally expensive.

It is best fit for error manifolds which are either convex or at least smooth.

Question 14. Which DL algorithm can be considered the best for face detection?

The finest face detection algorithms include ArcFacem Spherface, FaceNet, Cosface and Probablisit.

You May Also Read- Deep Learning Tutorial for Beginners

Advanced Deep Learning Interview Questions for Experienced

Get started with these advanced deep learning interview questions for experienced professionals.

Question 15. Is there any scenario where a DL model is built solely on linear regression?

In case the issue is represented by a linear equation, then a linear function can be used to build the deep networks as activation functions of every layer.

Question 16. How is artificial neural network backpropagation different from recurrent neural network backpropagation?

Advanced Deep Learning Interview Questions for Experienced

These are different as each node in RNN has an additional loop, while that is not the case in ANN. This loop adds a temporal component into the network, which enables capturing the sequential info from data. This is not possible in a generic ANN.

Question 17. What is meant by autoencoders? What are its different layers?

what is autoencoders

Autoencoders are a type of neural network. It has the condition that the dimension of the output layer and the input layer is the same. Basically, the number of input layer's input units is the same as the output layer's output units. It is also referred to as a replicator neural network because it duplicates/replicates data from the input to the output.

The layers of its architecture are-

  • Encoder
  • Code
  • Decoder

Question 18. What are the key applications of autoencoders?

Some key applications of autoencoders are-

  • Dimensionality reduction- The autoencoders convert the input into a reduced representation. This is stored in the middle layer, which is called code.
  • Image colorization- Autoencoders aid in converting a black and white image into a colored one.
  • Image denoising- This is where autoencoders are used to more specific information on the image's content.
  • Feature extraction- The imperative features in the input data that are hidden are learnt with autoencoders. This helps in reducing the reconstruction error.
  • Data compression- While these can be used for data compression, it hardly happens because of data specificity and lossy compression.

Question 19. How is deep learning different from ML?

deep learning and ML

Machine learning is AI's subset that enables the system to learn as well as grow from experiences. It does not have to be programmed regularly. It is being used extensively in cyber fraud detection, self-driving cars and face recognition right now.

Deep learning is an ML's subset wherein ANN and RNN are linked. Its algorithms are constructed just like Machine Learning algorithms but there are many more levels. It mimics the human brain and helps in tackling various complex problems.

It is a subset of ML.It is a subset of AI.
It generally works with millions of data points.It generally works with thousands of data points.
It uses neural networks for representing data.It uses structured data to represent data in a myriad of ways.
ML evolved into DL, which depicts the true depth of the former.ML has evolved from AI.

Question 20. What is a dropout?

What is a dropout?

Dropout in DL is a regularization approach. It aids in avoiding overfitting, which leads to enhanced generalizability. Ideally, a low dropout value, around 20-50 percent of neurons should be used. Too low probability will have no effect, while too high probability causes under learning of the network.

Roles and Responsibilities in Deep Learning

As a DL engineer, there are many roles and responsibilities in deep learning which are common. While every job profile is a little bit different and requires distinct skills and knowledge span, these are the few responsibilities that remain the same.

  • Conducting data engineering subtasks- This includes defining data requirements, labeling, augmenting, collecting, moving, cleaning and inspecting data.
  • Performing deployment subtasks- This includes setting a cloud environment for deploying the model, enhancing the response time converting the protyped code into a production code and saving bandwidth.
  • Modeling subtasks- This comprises reading research papers, training DL models, searching hyperparameters, and defining evaluation metrics.

Job Opportunities in Deep Learning

The DL market was valued at 49.6 billion USD in 2022. By 2030, it is expected to grow at a CAGR of 33.5%, which reflects how wide and vast the opportunities will be.

Here are some of the top job opportunities in this field that can be explored today. In the coming years, as this sector expands, more career paths will open up.

  • DL engineer
  • Data scientist
  • Software engineer
  • Research scientist
  • BI developer
  • AI engineer

Deep Learning Engineer Salary on the Basis of Skills

Deep learning engineer salary on the basis of skills in the US is $151.5k per annum.

In India, however, this salary ranges between ₹3 LPA to ₹24 LPA, with an average of ₹11 LPA.

This is an exceptionally high time for all technologies associated with AI. Since DL is also one of them, it is also experiencing many new trends and changes. Here are some of the top deep learning job trends in 2024.

  • AI Ethics & Governance

There is a continued concern regarding AI ethics and talks about better governance needs. This is where DL plays an imperative role in helping organizations utilizing AI in their everyday work.

  • Natural Language Processing

The world is becoming more dependent on voice assistance, text analytics applications and chatbots. These are almost everywhere and DL is one technology that is used extensively to make them better. Thus, jobs that include NLP using DL techniques are rising rapidly.

  • Industry-Specific Roles

Most leading industries today experience application of DL in one way or the other. Finance, healthcare, automotive and banking are some of the fields that showcase a huge need for professionals having knowledge of ML, DL and AI. There are plenty of job postings everywhere, which are a testimony to the growing demand.

  • Autonomous Systems

Everyone is raving about drones, self-driving cars and robotics. But what's actually behind them all is deep learning, which has enabled these systems to absorb and interact with their respective environments. As autonomous systems become more famous, the demand for these professionals rises too.

  • High Demand & Great Salary

There is no dearth of opportunities in this field since technology is becoming the core of most of our actions and tasks. The demand is greater than the supply for these professionals, which is also the reason behind the high salary packages they enjoy.

  • Growing Complexity

With the changing demands and better technical advancements, the DL models are also becoming more complex and larger. Consequently, the need for DL engineers and infrastructure engineers is growing too. These professionals have knowledge and skills in designing and optimizing systems. It aids in efficiently training and deploying these models.

Wrap-Up For Deep Learning Interview Questions

These deep learning interview questions and answers are perfect for anyone who is looking to expand their roots into this field. As a professional, intermediate or a beginner, one needs to have a complete understanding of the kind of questions that can be asked, and this blog has attempted to provide a glimpse into the top ones.

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