Deep Learning Interview Questions and Answers

Deep Learning Interview Questions and Answers from Codingcompiler – In this article we have prepared the most frequently asked Deep
Learning Interview Questions and Answers for beginners and experienced by covering all the core areas by professionals.

Deep Learning Interview Questions 

  1. What Is Deep Learning?
  2. What is a Neural Network?
  3. What’s the Difference Between AI, Machine Learning, and Deep Learning?
  4. How many layers does Neural networks consist?
  5. What Is Data Normalization, and Why Do We Need It?
  6. Why are deep networks better than shallow ones?
  7. What is the cost function?
  8. What is gradient descent in machine learning?
  9. Why do we use gradient descent?
  10. What Is the Boltzmann Machine?

Deep Learning Interview Questions and Answers

Q1) What Is Deep Learning?

Deep Learning involves taking large volumes of structured or unstructured data and using complex algorithms to train neural networks. It performs complex operations to extract hidden patterns and features (for instance, distinguishing the image of a cat from that of a dog).

Q2) What is a Neural Network?

A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.

Q3) What’s the Difference Between AI, Machine Learning, and Deep Learning?

AI, machine learning, and deep learning – these terms overlap and are easily confused, so let’s start with some short definitions.

AI means getting a computer to mimic human behavior in some way.

Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure things out from the data and deliver AI applications.

Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems.

Q4) How many layers does Neural networks consist?

The most common Neural Networks consist of three network layers:

1. An input layer

2. A hidden layer (this is the most important layer where feature extraction takes place, and adjustments are made to train faster and function better)

3. An output layer

Each sheet contains neurons called “nodes,” performing various operations. Neural Networks are used in deep learning algorithms like CNN, RNN, GAN, etc.

Q5) What Is Data Normalization, and Why Do We Need It?

Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values. For machine learning, every dataset does not require normalization.

Q6) Why are deep networks better than shallow ones?

There are studies that say that both shallow and deep networks can fit at any function, but as deep networks have several hidden layers often of different types so they are able to build or extract better features than shallow models with fewer parameters.

Q7) What is the cost function?

A cost function is a measure of the accuracy of the neural network with respect to the given training sample and expected output. It is a single value, nonvector as it gives the performance of the neural network as a whole. It can be calculated as below Mean Squared Error function:-


Where Y^ and desired value Y is what we want to minimize.

Q8) What is gradient descent in machine learning?

Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model.

Q9) Why do we use gradient descent?

The main reason why gradient descent is used for linear regression is the computational complexity: it’s computationally cheaper (faster) to find the solution using the gradient descent in some cases. So, the gradient descent allows to save a lot of time on calculations.

Q10) What Is the Boltzmann Machine?

A Boltzmann machine is a type of stochastic recurrent neural networks and Markov random field. Boltzmann machines can be seen as the stochastic, generative counterpart of Hopfield networks.

Frequently asked Deep Learning Interview Questions and Answers for Freshers

Q11) What Do You Understand by Backpropagation?

Backpropagation is a technique to improve the performance of the network. It back propagates the error and updates the weights to reduce the error.

Q12) What are the 2 layers of restricted Boltzmann machine called?

The two layers of a restricted Boltzmann machine are called the hidden or output layer and the visible or input layer. The various nodes across both the layers are connected.

Q13) Explain the concept of ‘overfitting’ in the specific field?

Overfitting is one of the most common issues that take place in deep learning. It generally appears when the sound of a specific data is apprehended by a deep learning algorithm. It also occurs when the particular algorithm is well suitable for the data and shows up when the algorithm or model indicates high variance and low bias.

Q14) Name the several initiatives used in the particular field

There are ample access ways to machine learning, but there are a certain amount of recorded skills that are mostly used in today’s industry.

1. Cognitive approach

2. Analyzing approach

3. Problem-solving

4. Allegorical approach

5. Approach to classification

6. Elementary approach

Q15) What is Fourier transform?

The Fourier Transform, on the other hand, applies to non periodic signals, e.g. a delta function. a single pulse (rectangular or otherwise). It is a method of expressing such signals in terms of frequency instead of time. In other words it transforms an aperiodic signal from the time domain to the frequency domain.

Q16) What are the prerequisites for starting out in Deep Learning?

Starting out in deep learning is not as difficult as people might make you believe. There are a few elementary basics that you should cover before diving into deep learning. Deep learning requires knowledge of the following topics:

Mathematics: You should be comfortable with probability, derivatives, linear algebra and a few other basic topics. Khan Academy offers a decent course covering almost all the above topics here.

Statistics: The basics of statistics are required for going forward with any machine learning problem. Understanding the concepts of statistics are essential because most of the deep learning concepts are derived from assimilating the concepts of statistics. You can check the online courses available here. 

Tool: A decent level of coding skills are required for implementing deep learning into real life problems. Coursera’s, Introduction to Data Science in Python is a decent course to start off with Python as a tool.

Machine Learning: Machine learning is the base for deep learning. One can not start learning deep learning without understanding the concepts of machine learning. You could go through Intro to Machine Learning or Andrew Ng’s course Machine Learning for a theoretical base.

By this Deep Learning Interview Questions and answers, many students are got placed in many reputed companies with high package salary. So utilize our Deep Learning Interview Questions and answers to grow in your career.

 Q17) Which data visualization libraries do you use and why they are useful?

 It is valuable to determine your views value on the data value properly visualization and your individual preferences when one comes to tools. Popular methods add R’s ggplot, Python’s seaborn including matplotlib value, and media such as and Tableau.

Q18) Where do you regularly source data-sets?

 This type of questions remains any real tie-breakers. If someone exists going into an interview, he/she need to remember this drill of any related question. That completely explains your interest in Machine Learning.

Q19) What is meant by a backpropagation?

It ‘s Forward to the propagation of data-set value function in order to display the output data value function.

Then using objective value also output value error derivative package is computed including respect to output activation.

Then we after propagate to computing derivative of the error with regard to output activation value function and the previous and continue data value function this for all the hidden layers.

Using previously calculated the data-set value and its derivatives the for output including any hidden stories we estimate error derivatives including respect to weights

Q20) What Are the Applications of a Recurrent Neural Network (RNN)?

The RNN can be used for sentiment analysis, text mining, and image captioning. Recurrent Neural Networks can also address time series problems such as predicting the prices of stocks in a month or quarter.

Frequently Asked Deep Learning Interview Questions and answers

Q21) What Is the Difference Between a Feedforward Neural Network and Recurrent Neural Network?

The main difference in RNN and Forward NN is that in each neuron of RNN, the output of previous time step is feeded as input of the next time step. This makes RNN be aware of time (at least time units) while the Feedforward has none. For example, in the handwritten digits classification, you have the input and output. There is no difference between time 1st or time 100th because the network has the same input and output. **RNN is usually used to describe a sequence** (can be time sequence or a context).

 For example, reading a big text file and output the next character. As you read more, the knowledge you accumulate through your previous timestep makes the network aware of the context. So RNN will have better performance than vanilla Feedforward because Feedforward has no better idea what to output, given all the previous outputs.

Q22) List some real-life applications that involve deep learning?

  • Google and Facebook are translating text into hundreds of languages at a time. This is being done through some deep learning models being applied to NLP tasks and is a major success story.
  • Conversational agents like Siri, Alexa, Cortana basically work on simplifying the speech recognition techniques through LSTMs and RNNs. 
  • Deep learning is being used in impactful computer vision applications such as OCR (Optical Character Recognition) and real-time language translation
  • Multimedia sharing apps like Snapchat and Instagram apply facial feature detection which is another application of deep learning.
  • Deep Learning is being used in the Healthcare domain to locate malignant cells and other foreign bodies in order to detect complex diseases.

Q23) List some commercial practical applications of ANN?

  • sales forecasting
  • industrial process control
  • customer research
  • data validation
  • risk management
  • target marketing

Q24) What are some applications of Recurrent Neural Network?

  • One to Many

This is the simple network with one input and multiple outputs. Example: It helps you caption an image, where the picture goes through the CNN model and then fed to the RNN.

  • Many to One

Example: It can be used in sentiment analysis and text mining, where you have a lot of text such as a customer’s comment and you need to gauge that what’s the chance that this comment is positive or negative. 

  • Many to Many

Translations such as Google translator and generating subtitles for the movies is an example of many to many types of network

Q25) State one of the finest procedures often utilized to overcome the issue of overfitting

Usually, the problem of overfitting can be interrupted with the help of increased data usage, but if the problem is still appearing, one can apply the method of ‘Isotonic regression.’

Advanced Deep Learning Interview Questions And Answers For Experienced

Q26) What Will Happen If the Learning Rate Is Set Too Low or Too High?

When your learning rate is too low, training of the model will progress very slowly as we are making minimal updates to the weights. It will take many updates before reaching the minimum point.

If the learning rate is set too high, this causes undesirable divergent behavior to the loss function due to drastic updates in weights. It may fail to converge (model can give a good output) or even diverge (data is too chaotic for the network to train).

Q27) What Are Hyperparameters?

With neural networks, you’re usually working with hyperparameters once the data is formatted correctly. A hyperparameter is a parameter whose value is set before the learning process begins. It determines how a network is trained and the structure of the network (such as the number of hidden units, the learning rate, epochs, etc.).

Q28) What Is Dropout and Batch Normalization?

Dropout is a technique of dropping out hidden and visible units of a network randomly to prevent overfitting of data (typically dropping 20 percent of the nodes). It doubles the number of iterations needed to converge the network.

Batch normalization is the technique to improve the performance and stability of neural networks by normalizing the inputs in every layer so that they have mean output activation of zero and standard deviation of one.

Q29) What does batch normalization do?

 Batch normalization was introduced in a 2015 paper. It is used to normalize the input layer by adjusting and scaling the activations.

Q30) Why is batch normalization important?

The basic idea behind batch normalization is to limit covariate shift by normalizing the activations of each layer (transforming the inputs to be mean 0 and unit variance). This, supposedly, allows each layer to learn on a more stable distribution of inputs, and would thus accelerate the training of the network.

Q31) What is weight initialization in neural networks?

Weight initialization is one of the very important steps. A bad weight initialization can prevent a network from learning but good weight initialization helps in giving a quicker convergence and a better overall error. Biases can be generally initialized to zero. The rule for setting the weights is to be close to zero without being too small.

Q32) What is autoencoder?

An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”.

Q33) What are the five popular algorithms of machine learning?

Here is the list of 5 most commonly used machine learning algorithms.

  • Linear Regression.
  • Logistic Regression.
  • Decision Tree.
  • Naive Bayes.
  • kNN.

Q34) What is an algorithm in machine learning?

Machine learning algorithms are programs (math and logic) that adjust themselves to perform better as they are exposed to more data. The “learning” part of machine learning means that those programs change how they process data over time, much as humans change how they process data by learning.

Q35) What is classification?

Classification is used to predict the outcome of a given sample when the output variable is in the form of categories. A classification model might look at the input data and try to predict labels like “sick” or “healthy.”

Q36) What is regression?

Regression is used to predict the outcome of a given sample when the output variable is in the form of real values. For example, a regression model might process input data to predict the amount of rainfall, the height of a person, etc.

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