Deep learning is one part of ML (Machine Learning) based entirely on artificial neural networks. A neural network aims to imitate the functioning of the human mind; this means a deep learning professional doesn’t have to program everything explicitly in a deep learning model. Deep learning engineers train models on specific training datasets and continue improvising until the model starts making correct predictions on the validation and testing datasets. A deep learning model can focus on its own accurate features as well with minimal input from programmers and engineers. They significantly help in solving the dimensionality issue. 

The IT industry has seen significant growth in demand for deep learning professionals. Its applications have grown to cover nearly every sector and business. Enterprises are looking for skilled professionals who understand machine learning and deep learning techniques and can create models that mimic human behaviour. These deep learning interview questions can help you crack even the toughest interviews to land a high-paying deep learning job. 

Top Deep Learning Interview Questions:

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

Deep learning is a component of Machine Learning. It works with structures similar to neurons, like artificial neural networks that mimic the human mind. On the other hand, Machine Learning is a subset of AI (artificial intelligence). It uses algorithms and statistics to train systems and machines with existing data, improvising over time with experience. 

Q2. What is a perceptron?

A perceptron mimics real neurons present in our minds. Perceptrons receive inputs from several entities and apply various functions to them, transforming them into the desired output. Perceptrons primarily perform binary classifications, which means they see inputs, compute functions based on the inputs’ weight and convert the data into the required result. 

Q3. Is Deep Learning better than Machine Learning? How?

Machine Learning is a larger concept than Deep Learning and helps solve several data and business problems. However, when it comes to multi-dimensional data, Deep Learning is more effective. With such large datasets, Deep Learning models are more effective since they are primarily designed for this purpose. 

Q4. What are the most popular applications of Deep Learning?

The applications of Deep Learning have increased significantly. Some of the most widely-known applications are:

• Automatic text generation
• Sentiment analysis
• Computer vision
• Object detection
•  Image recognition
• NLP (Natural Language Processing)

Q5. What does overfitting mean?

while working with deep learning, overfitting is a common problem. this is a situation where deep learning algorithms thoroughly go through datasets to get valid infor mation. it results in the deep learning model picking up noise instead of useful information, which causes low bias and high variance. as a result, the model becomes less accurate due to factors you can avoid quite easily. 

 Q6. What are activation functions?

An activation function is a Deep Learning entity that translates inputs into a functional output parameter. This function calculates the neuron’s weighted sum with the bias to decide whether it should be activated or not.

 Using activation functions provides a non-linear model output. Activation functions can be classified into several categories and types. These are:

• Softmax
• ReLU
• Linear
• Sigmoid
• Tanh

Q7. Why does Deep Learning use Fourier Transform?

Fourier transform is a package that deep learning models require to analyse and manage large volumes of data within a database. It takes in an array of data in real time and processes it efficiently, ensuring systems maintain high efficiency. Fourier Transform can also make a Deep Learning model more open to processing a wide range of signals.  

 Q8. What are the steps that go into training a Deep Learning perception?

Five fundamental steps help determine a perception’s understanding.’

1. Initialise weights and thresholds.
2. Provide the input.
3. Calculate the output.
4. Update the weight in every step.
5. Repeat 2 and 4 as needed.

Q9. What is the loss function used for?

The loss function measures accuracy while checking if a particular neural network has accurately learned from the given training data or not. It does so by comparing the training and testing data sets. 

This function is the leading measure of a neural network’s performance. In Deep Learning, a high-performing network has a low-loss function at any point during training. 

Q10. Which Deep Learning tools and frameworks have you used so far?

This is one of the most common Deep Learning questions asked in job interviews. It tells the recruiter which tools you are familiar with and how deep your expertise is. Some of the most widely-used Deep Learning frameworks today are:

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

Q11. What is the purpose of the Swish function?

The Swish function was created by Google as a self-gated function for activation. It has been among the most widely-used activation functions today ever since Google revealed that it could outperform every other activation function when it comes to computational efficiency.

Q12. What are autoencoders?

An autoencoder is an artificial neural network that learns without any manual intervention. These networks can map an input to the corresponding output automatically. As the name suggests, an autoencoder is made up of two entities:

- Encoder: This fits the input to the internal computation state.

- Decoder: This converts the computational state back to the output.

Q13. Can you list the steps to follow if one wants to use the gradient descent algorithm?

Using the gradient descent algorithm requires five key steps. These are

1. Initialising weights and biases for the given network
2. Sending input data via the network (input layer)
3. Calculating the error or difference between predicted and expected values
4. Changing the values in the neurons to minimise the loss of function
5. Performing multiple iterations while looking for the weights best optimised for efficiency

Q14. What are the differences between a single-layer perceptron and a multi-layer perceptron?

Single-layer Perceptron ¦ Multi-layer Perceptron

• Can’t classify non-linear data points ¦ Classifies non-linear data
• A limited number of parameters ¦ Can withstand a large number of parameters
• Less efficient with large volumes of data ¦ Highly efficient with large numbers of datasets

Q15. In the context of Deep Learning, what do you understand by data normalisation?

Data normalisation is a pre-processing step that helps in refitting data into a specific category. As a result, the network learns more effectively as its convergence is better while performing backpropagation. 

Q16. What is forward propagation?

Forward propagation refers to a situation where the input is passed to a hidden layer with its weight. In each hidden layer, the activation function’s output gets calculated till it’s time to process the next layer. It is known as forward propagation since the process starts at the input layer and then moves to the output layer. 

Q17. What is backpropagation?

Backpropagation is a scenario where the cost function is minimised by first checking how the value will change when biases or weights change in a neural network. You can easily calculate this change by understanding the gradient of each hidden layer. This process is known as backpropagation since the process moves backwards from the output layers to the input layer. 

Q18. What are hyperparameters in Deep Learning?

A hyperparameter is a variable that helps in determining a neural network’s structure. Hyperparameters also help understand the number of layers, the learning rate and various other parameters in the neural network.

Q19. How can a hyperparameter be trained in a neural network?

You can train hyperparameters using the following four components:

1. Batch size: This parameter represents the size of the input block. The batch size can be changed and divided into sub-batches depending on the need.
2. Epochs: Epochs describe the number of instances when training data is visible to the neural network for training. This is an iterative process, which means the total epoch numbers vary depending on the data. 
3. Momentum: Momentum is the component that helps understand the new few steps to take place when the existing data is being executed. Momentum helps in avoiding oscillations during training.
4. Learning rate: This is a parameter used to represent the time needed for networks to learn and update the parameters. 

Q20. What is the use of LSTM?

LSTM is an abbreviation for long short-term memory. It’s a kind of RNN that sequences strings of data. It is made up of feedback chains that help it function like general-purpose computational entities.

To improve your chances of clearing your Deep Learning interview, enrol in a Deep Learning training course to strengthen your fundamentals and get expert mentorship before the big day.

Enquire Now