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Deep Learning Interview Questions & Answers. A Complete Guide.

27 Mar 2023

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So you're applying for a job role in your dream company, or you're already selected for the interview process? And now, you want a little bit of help to crack your next interview round. This guide will be of complete help to you. Below mentioned are all the possible questions that can be asked related to deep learning in the interview. Without any delay, let's start with seeing the most asked questions and their possible answers. 

Q1. What is deep learning? 

In an interview that requires technical knowledge, chances are the very first question would probably be this. The recruiter is not expecting a definition as an answer. Try explaining the concept with examples. 

Deep learning takes structured and unstructured data and trains the neural network to extract the hidden patterns and trends in the data. For example, doctors and hospitals use the concept of deep learning to detect unusual cancer cells in patients. 

Q2. What is a neural network? 

Since you'll be mentioning neural networks in the last question, then the interviewer may cross-question you about this. 

A neural network just replicates how the human brain functions and duplicates that on the computer system. A neural network consists of three layers: 

  • Input layer to feed the data.
  • Hidden layer that works and processes the data and identifies trends and patterns.
  • Output layer to produce the output. 

Q3. Is machine learning and deep learning the same? If not, then what's the difference between the concepts? 

Machine learning is part of a wider concept of artificial intelligence that focuses on improving the performance of models and providing better output. 

Deep learning is part of machine learning that focuses on mimicking the human brain by forming a neural network. 

Q4. List some of the real-time applications of deep learning. 

Deep learning is already applicable to various concepts. Some of the examples are: 

  • The facial detection feature on phones uses the concept of deep learning. 
  • Various huge companies like Facebook and Instagram use deep learning concepts to transfer text from one single language to hundreds of different languages. 
  • The voice commands are being converted into simpler text by Siri and Alexa by using deep learning. 

There are numerous different examples of deep learning. 

Q5. What is forward propagation? 

Forward propagation is the process of feeding the data to the input layer. Here, the data is fed and is then worked upon by the hidden layer and activation function and is then finally transferred to the output layer. 

Q6. What is backward propagation?

Backward propagation is the process of going in the opposite direction from right to left, that is, from the output layer to the input layer. The methods are important as they can help in improving and enhancing the accuracy of the deep learning algorithm. 

Q7. Do you know what overfitting is in deep learning? 

Overfitting is a problem in deep learning which happens when a model tries to fit the whole training data into the output. This leads to ignorance of patterns and trends of new data. Overfitting can be caused due to large training duration. This leads to the model learning about noise and hindrances of training data and focusing only on that. 

Q8. What is the opposite of overfitting data, and what does it mean? 

Underfitting in deep learning is the opposite of overfitting. Sometimes, to avoid the problem of overfitting, the training of data is stopped at an early stage. This leads to failure in the identification of trends and patterns in the data, causing underfitting. 

Underfitting leads to inaccuracy in the output and provides a false prediction as the solution. 

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Q9. What is perceptron? 

Perceptron works similarly to the human brain. It is responsible for receiving directions, working on them, and providing the output. Perceptron performs binary classification; it receives inputs, applies functions to them, and finally provides the output. 

Q10. What is Data Normalization? 

Data normalization is basically the preparation of data before using it as input data. The process involves cleaning data where unnecessary data is removed, and unstructured data is arranged so that data appears to be uniform in nature. 

The process of data normalization is beneficial as it helps in reducing the volume of data, keeps relevant data, and removes duplicate data.

Q11. What is the activation function? 

A neuron's activation status is determined by an activation function. To evaluate whether the neuron's input to the network is important or irrelevant to the prediction process, the activation function performs a few straightforward mathematical operations.

Q12. How to avoid the problem of overfitting and underfitting? 

Different techniques are used to avoid the problem of overfitting. Some of them are: 

  • Cross-validation: Divide your data into two parts, one for training and another for testing, to make sure overfitting doesn't come. 
  • Early stopping: Avoid the memorization of data by the model by stopping the training of data at the right moment. 

To avoid the problem of underfitting, one can try the below-mentioned techniques: 

  • Provide more data for training. 
  • Maximize the training time for the model. 
  • Increase the parameters of the model. 

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Q13. What is Transfer learning? 

The next common question for ml interview questions is about transfer learning. Transfer learning is the process of using a machine learning model that has already been trained to solve a separate but connected problem. The general concept is to apply what a model has learned from a task with a lot of labeled training data to a new task with little to no training data. 

We begin the learning process using patterns discovered while completing a comparable task, as opposed to starting from scratch.

Let’s see the next question in this guide of interview questions on deep learning.  

Q14. What is a Multi-Layer perceptron?

MLP, short for multi-layer perceptron, is a deep learning method that uses forward propagation. A directed graph connecting the input and output layers of an MLP is made up of multiple layers of input nodes.

Q15. Do you know anything about feature engineering? 

Feature engineering in deep learning is basically working with data. The data is looked at, monitored, selected, and then converted into certain features which are understood by machine learning models. 

So to obtain good results from the models, it is important to label the data with correct features, and thus the step of feature engineering is important for the process. 

Q16. Tell me what you know about types of activation functions. 

This is basically a practical aspect of the concept of deep learning. So, if the recruiter asks you this, chances are he/she is checking your practical exposure. You can mention a few common types of activation functions. If you know, you can even add a small description to each type. 

  • Sigmoid function: This function is used in feedforward and is used for prediction-based output requirements. The output of the function ranges from 0 to 1. The quantity of input will decide the value of output. 
  • Hyperbolic tangent function: The function is represented by tanh and is very much similar to the sigmoid function. Here the output ranges from -1 to 1. 
  • Softmax function: The function transforms the vector of K real values that total one by using the softmax function. The values can be positive, negative, zero, or higher than one, into values between 0 and 1, allowing them to be understood as probabilities.

Q17. What is CNN? 

CNN stands for a convolutional neural network. A CNN is a particular type of network design for deep learning algorithms that are utilized for tasks like image recognition and pixel data processing. Although there are different kinds of neural networks in deep learning, CNNs are the preferred network architecture for identifying and recognizing objects. 

One of the common methods of recruiters is to cross-question you based on your previous answer. So for deep learning interview questions for fresher, this can definitely be the next question. 

Q18. Tell me about the different layers of CNN. 

There are four different layers in the architecture of CNN, namely: 

  • Convolutional layer: The first layer's job is to determine whether a certain set of features is present in the input images.
  • ReLU layer: It gives the network nonlinearity and turns all the negative pixels into zero. A rectified feature map is produced as a result.
  • Pooling layer: This kind of layer, which receives many image features and applies the pooling operation on each of them, is frequently used between two layers of convolution.
  • Fully connected layer: A new output vector is created by the final CNN layer after receiving an input vector.

Q19. What is the loss function, and what is it used for? 

A loss function analyses how effectively the neural network models the training data by comparing the target and predicted output values. Your loss function would produce a greater value if your forecasts were completely incorrect. If they're decent, it will produce a lower number.

Q20. What do you understand by hyperparameters? 

Hyperparameters are parameters that affect how the network is trained and its topology (for instance, the number of hidden units) (Eg: Learning Rate). They are set before the model is trained, that is, before the weights and biases are optimized.

Q21. What is autoencoder? 

Self-supervised machine learning models called autoencoders are employed to recreate input data to reduce its size. These models are referred to as self-supervised models since they are taught as supervised machine learning models and then operate as unsupervised models during inference.

Q22. What is gradient descent? 

An optimization approach called gradient descent is frequently used to train neural networks and machine learning models. These models gain knowledge over time by using training data, and the cost function in gradient descent especially serves as a barometer by assessing the accuracy of each iteration of parameter changes.

Q23. What do you mean by RNN?

An artificial neural network that employs sequential data or time series data is known as a recurrent neural network (RNN). These deep learning algorithms are included in well-known programs like Siri, voice search, and Google Translate. They are frequently employed for ordinal or temporal issues, such as translation software, natural language processing (NLP), voice recognition, and image captioning.

Make sure, that you not only know about the concepts of deep learning but also know what are its applications, and how are they used, as it can be asked in machine learning deep learning interview questions

Q24. What are some of the applications of autoencoders? 

Following are some of the applications of autoencoders: 

  • With autoencoders, any black-and-white image may be transformed into a colored image. 
  • It generates the result by reducing any noise or pointless interruption and extracts only the necessary characteristics from an image.
  • Dimensionality Reduction: The reconstructed image has fewer dimensions but is otherwise identical to our input. It aids in delivering a comparable image with a lower pixel value.

Q25. Why do you use the word "dropout"?

A low-cost regulatory method called dropout is used to lessen overfitting in neural networks. At each training stage, a set of nodes is randomly removed. As a result, we developed a unique model with shared weights for every training case. It is a type of average model. This concludes our discussion of Deep Learning Interview Questions. We think that this set of questions will be sufficient to get you through any Deep Learning Interview, but if you're going for a particular job, you'll also need to have a strong understanding of the pitch because most positions require specialists.

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