45+ Deep Learning Interview Questions And Answers In 2025

The field of deep learning has gained a lot of popularity in recent years. Deep learning applications have spanned across every industry present as well as emerging. Companies nowadays are on a hunt for professionals that have relevant expertise to design realistic models with the help of Deep learning techniques.

In this blog, we have compiled a list of the most important deep learning interview questions for beginners, intermediate, and advanced-level candidates. In addition to this, we have also included some additional deep-learning questions to further enhance your preparation for the interview. These questions cover different levels of expertise and have a high probability of being asked during a deep-learning interview.

Introduction to Deep Learning

Deep learning refers to a kind of machine-learning approach that is used to train machines to perform tasks that we, as humans, do instinctively. In this whole process, a complex computer model is developed to be used to analyze text, sounds, images, etc. This model is further used to execute various tasks under several categories.

Deep learning algorithms provide cutting-edge precision and sometimes even place humans behind in their performance levels. The deep learning models are trained with the help of huge data quantities and multi-layered neural networks to get the best results.

Deep Learning Interview Questions For Beginners 

If you’re a college graduate and have no practical expertise in the field of Deep Learning, you will face some fundamental questions during the interviews. These questions can be very basic but are important for any interview. Let’s take a look at some of these questions.

Q1. What is the difference between Deep Learning and Machine Learning?

The following are the differences between deep learning and machine learning:

• Machine learning is a subset of artificial intelligence. This field uses several algorithms and statistical methods to train the machines. The performance of such machines improves over time with the data provided by humans.
• On the other hand, deep learning technology is specially designed to mimic human brains. They use elements such as neurons to build an efficient neural network.

Q2. What is Perceptron in Deep Learning?

Perceptron is an important concept in deep learning. It refers to a single neuron present inside the brain. Also, it is used to collect input from several sources. It further transforms those inputs into output with the help of neural functions. Perceptrons are most frequently used in the field of binary categorization. 

Q3. What makes Deep Learning superior to Machine Learning?

Machine learning has emerged as such an important field in recent years. It can solve any of the problems we encounter in our daily life in a very easy and efficient manner. However, deep learning has an upper hand over machine learning when we talk about dealing with large amounts of data. This data can also contain an excessive number of parameters. A deep learning system can easily work with such types of huge data.

Q4. What constitutes some of the greatest deep learning applications?

The several important characteristics of deep learning make it an excellent choice for domains such as Computer Vision, Sentiment Analysis, Natural Language Processing, Automatic Text Generation, Image Recognition, as well as Object Detection.

Q5. What does the term “overfitting” indicate?

A very common issue that comes into play when we apply deep learning algorithms is called overfitting. Overfitting occurs when a model detects noise instead of usable data. This phenomenon takes place due to excessive searching within the dataset. This search ultimately results in high variation and reduced accuracy. 

If you’re interested in learning more about deep learning and its applications, enrolling yourself in a deep learning course can help you master all the concepts related to the subject.

Q6. What do activation functions mean?

Activation functions play an important role in deep learning concepts. These functions are used to transform inputs into useful output parameters. They are also used to compute weighted total with bias. This concept helps us in determining whether a neuron requires activation or not.

Q7. What is the purpose of the Fourier Transform in Deep Learning?

The Fourier Transformation in deep learning is a powerful tool used to organize vast amounts of data with databases. A Fourier Transform can handle an array of data in real-time and makes the learning model even more flexible and efficient.

Q8. What are the stages in Deep Learning for training a perceptron?

There are five significant steps involved in the training procedure of a perceptron: 

• Set threshold and weights.
• Provide input to the system.
• Determine or calculate the output.
• Update weights at each step.
• Repeat steps 2 to 4.

Q9. What is the purpose of the loss function in Deep Learning?

The loss function in deep learning is used to indicate the accuracy and determine whether a neural network is learning properly from its training data or not. This is achieved by the means of comparing training datasets with the testing datasets.

Q10. What Deep Learning systems or techniques have you utilized in the past?

This is among the most popular deep-learning interview questions. You can answer this question based on your experience with various tools.

Some of the best deep-learning frameworks available in the market are:

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

Q11. What does the Swish function do?

Google came up with the Swish function. The Swish function is a self-gated activation feature in machine learning. This function can surpass all other activation methods when it comes to the level of processing efficiency. 

Q12. What do you mean by Autoencoders?

Autoencoders are mainly referred to as artificial neural networks. Their services are developed autonomously. These types of neural networks can efficiently learn by translating data inputs provided to them into the associated outputs.

Q13. Distinguish between a Single-layer and a Multi-layer Perceptron.

The following are the key differences between single-layer and multi-layer perceptron:

Single-layer Perceptron	Multi-layer Perceptron
A single-layer perceptron is not able to classify non-linear data points occurring inside any program.	A multi-layer perceptron can easily classify non-linear data points that are present inside any program.
A single-layer perceptron has the ability to only consider a few criteria.	A multi-layer perceptron is capable enough to withstand a wide range of given parameters.
A single-layer perceptron is less efficient when it is about dealing with big and significant amounts of data.	With large datasets, a multi-layer perceptron has the ability to be extremely efficient.

Q14. What is Deep Learning data normalization?

Data Normalization in deep learning is considered a preprocessing operation. This operation is primarily used to arrange data inside a given range by the user. As a result, a network with data normalization can be trained more effectively as compared to other networks. This is due to the fact that data normalization provides an improved convergence due to the presence of backpropagation.

Explore our data science course with placement guarantee and start your career as a data scientist.

Q15. What do you mean by Forward Propagation?

Forward propagation in deep learning is referred to as a technique in which inputs that contain weights are directly transferred to the concealed layer. This process is called forward propagation because it simply originates at the input level and progresses toward the final output level.

Q16. What exactly is Backpropagation?

Backpropagation is a technique in deep learning. It has the ability to minimize the cost equation in a function. It does so by observing how the value of the cost equation changes when biases and weights change in the function.

Q17. What are Deep Learning hyperparameters?

Hyperparameters are basically the factors, which are used to determine the structure of a neural network in the deep learning technique. They also help us to understand various neural network parameters. For example, the degree of hidden layers and the learning rate.

Deep Learning Interview Questions And Answers For Intermediate

Let’s now take a look at some of the most frequently asked intermediate-level interview questions on deep learning. These questions are generally asked by candidates who have little experience in the deep learning field.

Also Read: Deep Learning Projects

Q18. What does the term “dropout” signify in Deep Learning?

A dropout is an approach in deep learning, which is significantly designed to prevent a model from overfitting. While we use this approach, the impact on learning will be modest whenever the dropout rate is too low. On the other hand, if the dropout is overly high, the model will under-learn. The under-learning of the model results in its reduced efficiency.

Q19. What are tensors in deep learning?

Tensors are defined as arrays of multiple dimensions. These arrays can be used to display data with the help of a deep learning approach. The syntax for a tensor function is quite simple to learn and understand by any individual. Tensors are widely used in the deep learning domain due to the fact that they provide a high-degree structure inside any of the computer languages.

Q20. What does model capacity mean in Deep Learning?

In deep learning, the model capacity approach is referred to as the ability of a model by which it can process a wide variety of given inputs.

Q21. What do you mean by a Boltzmann machine?

A Boltzmann machine is a type of natural network in deep learning. This network contains recurrent connections. It performs its functions with the help of binary decisions and biases. These neural networks can be linked together to form a deep belief system. 

Q22. Tell us some benefits of utilizing TensorFlow.

TensorFlow provides a variety of benefits, some of which are:

• TensorFlow exhibits a high degree of adaptability and platform independence.
• Training technique based on CPU and GPU.
• TensorFlow supports auto differentiation and related features.
• TensorFlow can also very easily handle threads and asynchronous computing. 
• TensorFlow is an open-source platform and has a sizable community.

Q23. In Deep Learning, what do you mean by a computational graph?

A computational graph in deep learning is defined as a set of procedures that take inputs and organize them into nodes present inside an ordered graph. 

Q24. What in particular is a CNN?

CNNs are referred to as convolutional neural networks. These networks have the ability to accomplish image and visual processing inside the neural networks. These neural network classes can also input as well as interpret multi-channel images very efficiently.

Q25. In Deep Learning, what do you understand by an RNN?

RNNs are called recurrent neural networks. They are an important type of artificial neural network. RNNs can easily handle data sequences, text, handwriting, genomes, and other such types of data. The RNNs make use of the backpropagation approach to train machines in the field of deep learning.

Q26. What do you mean by vanishing gradient in the context of RNNs?

When we use recurrent neural networks in deep learning, a situation called ‘vanishing gradient’ sometimes appears. Gradients present at each step of the neural network route become less extensive when the network covers its backward iterations. This phenomenon is called a vanishing gradient.

Q27. What is the effect of an exploding gradient descent?

Exploding gradients basically represent a problem, which in turn, results in the occurrence of clumping gradients. In a deep learning training method, exploding gradients result in a significant number of weight updates within the model. 

Q28. What is the function of LSTM?

LSTM is referred to as long short-term memory. This memory term means a type of recurrent neural network, which can be utilized to sequence data items stored inside a string.

Q29. Where do autoencoders come into play in deep learning?

Autoencoders provide us with a wide range of applications in the real world. Among the most popular applications, some are mentioned below:

• They are used to color black-and-white photos.
• They can also reduce image noise.
• They help to reduce dimensionality as well.
• They feature omission and variation in models.

Q30. What are the different types of Autoencoders?

Autoencoders are basically classified into four major types. They are:

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

Q31. What do you mean by a Restricted Boltzmann Machine?

An RBM, also called Restricted Boltzmann Machine, is a type of common undirected graphical algorithm. It is one of the most-used and popular techniques in the deep learning domain nowadays.

Advanced Deep Learning Interview Questions For Experienced

Let us now move on to look at some advanced Deep Learning questions specifically designed for experts. 

Q32. Tell us some limitations of Deep Learning.

Deep Learning has a few drawbacks. They are mentioned below:

• Deep Learning networks require a massive quantity of data to train properly.
• Deep Learning principles can be difficult to implement at times.
• In many circumstances, achieving a high level of model effectiveness is difficult.

Q33. What are gradient descent variants in Deep Learning?

Gradient descent has three major versions. They are listed below:

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

Q34. What do you understand by deep autoencoders?

Deep autoencoders are a subset of conventional autoencoders. In deep autoencoders, the initial layer is used to execute the first-order function in the input parameters. The subsequent layer will then handle the second-order operations, and so on.

Q35. What is the purpose of the Leaky ReLU function in the context of Deep Learning?

Leaky ReLU is also called LReL. It is an easy way to manage a deep-learning operation. An LReL allows the conveyance of small-sized values. These values are negative when the input value of the system is lower than zero.

Q36. What are some Deep supervised learning algorithms?

Deep Learning has three primary supervised learning algorithms. They are:

• Artificial neural networks or ANNs
• Convolutional neural networks or CNNs 
• Recurrent neural networks or RNNs

Q37. What are some instances of Deep Learning unsupervised learning algorithms?

Deep Learning uses three primary unsupervised learning algorithms:

• Boltzmann machines 
• Automatic encoders
• Self-organizing maps

Most Commonly Asked Interview Questions On Deep Learning

Let’s have a look at some additional Deep Learning neural network interview questions that will help you enhance your preparation further.

Q38. Is it possible to reset all weights of a network’s components to zero?

Yes, you can start with zero initialization to achieve this. 

Q39. What processes take place in the operation of an LSTM network?

The operation of an LSTM network consists of three major steps. These steps are:

• The network gathers whatever data it needs to recall. It also determines what it should forget.
• The first step is then used to update the cell state values.
• The network further computes and evaluates which portions of the present state should become output.

Q40. What are the programmable parts in TensorFlow?

TensorFlow allows users to program three different components. These components are mentioned below:

• Variables 
• Constants
• Placeholders

Q41. What do bagging as well as boosting represent in Deep Learning?

Bagging is the process in which a set of data is divided and distributed arbitrarily into several bags. This process is basically done to train a model.

Boosting is the process of intentionally incorporating erroneous data points. These points are used to force the algorithm to deliver the wrong output. 

Q42. What are GANs in Deep Learning?

Deep Learning makes use of generative adversarial networks to produce deep generative modeling. The GAN is used to represent an unsupervised activity in which the input data patterns are discovered. These patterns are then used to derive the model output.

Q43. What makes GANs so popular?

A variety of applications, nowadays, use generative adversarial networks or GANs. When it comes to interacting with photos, they provide a lot of momentum and prove to be quite efficient in their process.

Q44. What is the meaning of Hyperparameters?

This is another one of the commonly asked deep learning interview questions. As the name suggests, a hyperparameter is a parameter, which carries a value. This value is determined before the beginning of the learning procedure. It influences how a deep system is taught as well as the structure of the system.

Q45. What happens if the learning rate has been adjusted to too low or too high?

If the learning rate of a model is set to be excessively low, model training will happen quite slowly. This occurs because we are only making minor changes to the model weights. 

When the learning rate is adjusted too high for a model, the loss function will demonstrate a divergent behavior, which is unwanted for the users. This phenomenon happens because of sudden weight changes in the model.

Q46. What is the meaning of Vanishing and Exploding Gradients?

In the process of training a reinforcement neural network, the slope can grow too little or too big. This phenomenon makes the training procedure quite challenging for any model. When the slope becomes too slight, the issue is referred to as the Vanishing Gradient. On the other hand, if the slope grows exponentially and does not demonstrate diminishing, this phenomenon is known as the Exploding Gradient.

Conclusion

To reserve a good position in the deep learning industry, engineers must go through these deep learning interview questions. This will help them understand the concepts on a deeper level. A lot of people consider deep learning as a top occupation for this era. Deep learning has created numerous jobs, which has caused quite a stir in the marketplace.