Deep Learning Interview Questions are the best way to assess your knowledge in the domain before you sit at any technical interviews.
These questions are majorly asked for various data science and machine learning job roles.
Presented here are 20 top deep learning interview questions candidates can review when they prepare for interviews at tech companies. Thoroughly reviewing these deep learning interview questions can also aid in interview prep training to excel in interviews at FAANG companies.
A Few Basic Deep Learning Interview Questions
This section presents a few basic deep learning interview questions one can look into to prepare for interviews at various technology companies.
- What is Deep Learning?
Deep Learning is a subset of machine learning where data is used to train artificial neural networks so that they can recognize and differentiate between patterns and representations. Once trained, these neural networks are able to discover features from the data they are trained on their own without being programmed to perform such functions.
Unlike deep learning, in machine learning significant manual effort is required to select and extract relevant features from the raw data before the model can be trained. - What Is a Neural Network?
A neural network is a computational model that works on the principle of the biological neural network of the human brain. It consists of layers of interconnected nodes called neurons: input layer, intermediate or hidden layer, and output layer. Each connection between nodes has a weight associated with it.
Raw data is passed from the input layer, gets processed through intermediate or hidden layers using weighted connections and activation functions, and produces an output in the output layer. - What Are the Basic Building Blocks of a Neural Network?
Basic building blocks of a neural network include four elements:
- Neurons: Basic units that perform functions on received inputs to produce relevant outputs.
- Weights and Biases: Weights and biases are linked with each neuron. During training, these weights and biases are adjusted to optimize the network's performance.
- Activation Function: With this function, non-linearity is introduced into the network, which is essential for the neural networks to learn complex relationships.
- Layers: There are various types of layers within which neurons are organized, with each performing specific computation, These layer types are input layer, hidden layers, and output layer.
- What Are Some Examples of How Deep Learning is Used in Business and Industry?
Deep Learning is used across a range of real-world applications:
- Healthcare: Medical Imaging, Drug Discovery, Personalized Treatment and Disease Prediction
- Finance: Fraud Detection, Algorithmic Trading and Credit Scoring
- Retail: Customer Personalization, Inventory Management and Visual Search
- Manufacturing: Predictive Maintenance, Quality Control and Supply Chain Optimization
- Automotive: Driverless Vehicles and Traffic Control Systems
- Energy: Energy Consumption Forecasting and Smart Grid Management
- Where Should We Choose Deep Learning Over Machine Learning?
Deep Learning is generally preferred where data has a high degree of complexity. When problems require massive amounts of data to process or data that requires subtle patterns to interpret deep learning is generally preferred over machine learning.
Some of the examples of deep learning applications are:
- Classifying images into predefined categories
- Translating of text from one language to another through voice of text inputs
- Object detection and lane detection in navigation systems
- Analyzing medical images and records to predict disease outcomes
- Prediction of stock patterns
Deep Learning Interview Questions for College Graduates
Presented are a few deep learning interview questions that can help college graduates to confidently answer questions during tech-intensive interviews for entry-level machine learning deep learning positions.
1. What is Overfitting in the Context of Deep Learning?
When the model gives correct output when it’s tested against trained data but fails to generate correct output on new data, this phenomenon is called overfitting. In this context, the model is said to be overfitted.
Such a situation occurs when:
- The model is highly complex in nature (model has a large set of parameters; can represent detailed patterns in trained data)
- The model has overtrained in the specific data set
- The model’s training data has become inapplicable and it requires retraining
2. What is Underfitting in the Context of Deep Learning?
When the model fails to generate correct output on trained as well as new data, it is called underfitted model. Such a problem occurs when:
- Model training time is accurate
- Training data is limited
3. Provide Information on Common Activation Functions Used in Deep Learning?
Some common activation functions are:
- ReLU (Rectified Linear Unit): Is linear for all values greater than zero, or more precisely: \( f(x) = \max(0, x) \)
- Sigmoid: Maps the incoming inputs to a range between 0 and 1: \( f(x) = \frac{1}{1 + e^{-x}} \)
- Tanh (Hyperbolic Tangent): Maps the incoming inputs to a range between -1 and 1: \( f(x) = \frac{e^x - e^{-x}}{e^x + e^{-x}} \)
4. What do you understand about gradient clipping in the context of deep learning?
Gradient clipping is a deep learning technique that resolves the issue of exploding gradients by capping the maximum value of gradients during training. By doing so, the learning process gets stabilized and the model parameters are updated in a controlled manner, leading to more effective and reliable training.
Gradient clipping involves setting a threshold for the gradients. If the gradients exceed, they are scaled down to keep them in a desired range.
Two types of clipping are:
Clipping by Value: Minimum and a maximum threshold values are defined. Any gradient exceeding this range is clipped to the threshold value.
Clipping by Norm: Clipping by norm involves setting a maximum threshold for the norm of the gradients. First, the norm of the gradient vector is calculated. If it exceeds a specified threshold set in the beginning, the gradient is scaled down to meet this limit.
5. What is the vanishing gradient problem and how does it affect training in deep neural networks?
During the training of deep neural networks, as the gradients propagate backwards through many layers at times they become extremely small. This problem can cause earlier layers to learn very slowly or not learn at all.
As a result of this problem, the deep neural network delivers suboptimal performance and slow convergence.
Deep Learning Interview Questions for Engineers
Now let’s look at a few deep learning interview questions that can benefit engineers in their interview preparation.
- What type of a neural network is used in deep learning regression using Keras for TensorFlow?
Here are the aspects to consider when deciding the type of neural network to selected in deep learning regression using Keras-TensorFlow
- Have good knowledge of the available data and then decide the best model for it.
- When deciding on the neural network problem it’s also important to consider whether it is a linearly separable problem or not
- It is always better to start with a simple model like multi-layer perceptron (MLP) that has just one hidden layer unlike Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) Neural Network, or Recurrent Neural Networks (RNN) that require configuring the nodes and layers.
- MLP is considered the simplest neural network because the weight initialization is not sensitive and there is no need to define a structure for the network beforehand.
- What Causes the Exploding Gradient Problem to Occur?
During the training of the model, when the model weights grow exponentially and become unexpectedly large, the exploding gradient problem occurs. For example, if a neural network has n hidden layers, n derivatives are multiplied together.
If the weights that are multiplied are greater than 1 then the gradient’s value increases exponentially and becomes greater than the usual one. Eventually, it explodes as one propagates through the model.
Under this situation, the output becomes exponentially larger thus hindering the training of the model and impacting its overall accuracy. This situation is called the exploding gradients problem.
It’s a serious problem because effective learning of the model is hindered resulting in poor performance and high loss. Solutions to the exploding gradient problem are gradient clipping, weight regularization, or use of LSTMs.
- Gradient Clipping: Set a threshold for gradient values. When the threshold is exceeded, gradients are scaled down to bring them within the limit and prevent the gradients problem from occurring.
- Weight Regularization: With methods such as L2 regularization, a penalty is added to large weights in the loss function. This ensures that the network keeps weights under control.
- Long Short-Term Memory Networks (LSTMs): LSTMs are recurrent neural network (RNN) types specifically designed to mitigate the exploding gradient problem, particularly in sequence modeling tasks. They work on gating mechanisms to control the flow of information and gradients, ensuring that learning over long sequences is stable.
- What are the Differences Between Batch Normalization and Layer Normalization?
Here are a few key differences between Batch Normalization and Layer Normalization
Also Watch: Mock Interviews on Deep Learning
- What Are the Reasons Behind the Introduction of Non-linearities in a Neural Network?
When we refer to incorporation of non-linearity in a neural network we mean to incorporate non-linear activation functions within the network's neurons or units.
This procedure is important because if a neural network is composed of only linear operations, it would effectively behave like a linear model. As a result, its capacity would be limited to learn complex patterns and relationships in data.
Now let’s look at the reasons behind introduction of non-linearity in a neural network:
Without the introduction of non-linearity, the neural network would only be able to represent linear transformations of input data With non-linear activation functions, the neural network gets the capacity to model complex, non-linear relationships. This enables it to learn even from the complex patterns in the data.
With the inclusion of non-linearities, the expressive power of neural networks gets enhanced. This results in making the neural networks capable of approximating a wide range of functions. In addition, solving complex tasks like image recognition, natural language processing becomes possible for them.
- Provide the steps to implement a gradient descent algorithm?
Here are the steps to implement a gradient descent algorithm for neural networks
Step 1: Initialize Model Parameters: First initialize the model parameters (weights and biases), either randomly or by using a specific initialization strategy.
Step 2: Forward Pass: Use the current parameters to pass the input data through the neural network layers to calculate the predicted output
Step 3: Compute Loss: Compare the predicted output to the true labels using a chosen loss function (for example: mean squared error for regression) to calculate the loss.
Step 4: Backward Pass: Now, with respect to each parameter in the network, compute the gradients of the loss function. One can apply the chain rule backward through the network to propagate the error.
Step 5: Update Parameters: Adjust the parameters in the direction that minimizes the loss function. You can do so by using the gradient descent update rule:
Step 6: Repeat: Iterate the above steps until the loss converges to a minimum value or a fixed number of epochs.
Deep Learning Interview Questions for Data Scientists
Here are a few deep learning interview questions that data scientists can benefit from during the tech interview rounds for the role of data science.
- What are Hyperparameters? Name and Provide Details of a Few of Them Used for Training a Neural Network.
Hyperparameters are parameters that are set prior to the training process and control the training process itself. However, they cannot be learnt through the training process and can only be specified by the user itself.
Examples of hyperparameters include the learning rate, number of nodes, number of epochs, batch size, architecture of the neural network and regularization techniques.
Number of nodes: Nodes in the input layer of the neural network
Learning rate: Rate at which weights are adjusted during training
Number of epochs: Number of complete passes that an algorithm can perform for training
Batch size: Number of training examples utilized in one iteration of the model's training process
Architecture of the neural network: The organization and connection of various components of a neural network is called the architecture of the neural network.
Architecture of the neural network includes the three layer types of the neural network: input layer, hidden layers and output layer.
In addition it includes a number of hidden layers, a number of neurons per layer, activation functions i.e. functions applied to the output of each neuron in a layer to introduce non-linearity into the mode and how the connections are connected to every neuron.
Regularization Techniques: Methods used to prevent overfitting are regularization techniques. A few regularization techniques are: L1 Regularization, L2 Regularization, Elastic Net, Dropout, Early Stopping, Batch Normalization, Data Augmentation, Weight Constraints and Drop Connect.
Optimizers: Optimizers are algorithms or methods used to adjust the parameters (weights and biases) of a neural network to get its learning rate right. Optimizers play an important role when it comes to ensuring the efficiency and effectiveness of training a machine learning model
- What Are Autoencoders? Explain Their Main Parts, Structure and Types.
Autoencoder is a neural network architecture that compresses the input data down to its essential features. Autoencoders consist of Autoencoders consist of two main parts:
Encoder: Compresses the input data into a lower-dimensional representation. The layers of the encoders reduce the input data size.
Decoder: Reconstructs the input data from its encoded representation compressed by encoders. The layers of the decoder restore the compressed data.
Structure of Autoencoders
- Input Layer: Takes the original (raw) data as input.
- Hidden Layers (Encoder): Compress the dimensionality of the input data
- Bottleneck Layer: Contains the compressed version of input data
- Hidden Layers: Layers that reconstruct the data
- Output Layer: Produces the reconstructed data which matches the original input data to its closest proximity.
Types of Encoders
- Vanilla Autoencoders: Contain simple encoder and decoder. All other layers are absent.
- Sparse Autoencoders: Along with encoders and decoders, hidden layers are present but most of their neurons are inactive.
- Denoising Autoencoders: Takes the corrupted version of the input, removes noise from the same and reconstructs its clean version.
- Variational Autoencoders (VAEs): Encodes the input data into a distribution rather than a single point, making them suitable for generative tasks.
- Contractive Autoencoders: Autoencoders learn more stable representations of the input data by penalizing any change caused by small variations in the input during encoding.
- Explain learning rate in the context of neural network models. What happens if the learning rate is too high or too low?
Learning rate regulates the learning curve of a neural network model. It controls the size of the update made to the weights during training. An appropriate learning rate must be chosen to ensure that the model is learning properly without proceeding too slowly or excessively steadily.
Learning rate is also called step size. As a hyperparameter, the learning rate can have substantial influence on the performance of the neural network.
It’s a scalar value that determines the size of the steps taken in the direction of the negative gradient during backpropagation.
With backpropagation, the error between the predicted and the actual outputs is propagated backwards through the network to update the weights.
Initially, random weights are chosen and gradually they are adjusted to minimize the loss incurred during the training of the neural networks.
The weights are updated on the basis of the gradient of the loss function. The equation provided below explains the correlation between weight, gradient of the loss function and learning rate.
θ=θ−α⋅∇J(θ)
Where θ is the weight
Gradient of the loss function is ∇J(θ)
α is the learning rate
If the learning rate is high or low, problems may occur in the learning performance of the network:
High learning rate: When the learning rate is high, the model takes large steps to attain minimum loss, which can cause it to overshoot and diverge from its target. This problem can make the learning model unstable, leading to erratic updates and possibly a failure to converge to the minimum.
Low Learning Rate:: When the learning rate is low, the model takes small steps, leading to slow convergence. This can lead to making training a time-consuming process as there will be too many iterations to reach to the minimum. This can also lead to the model getting stuck resulting in its failure to reach the minimum.
- Why should we use Batch Normalization?
Batch normalization is a technique used to enhance the training of deep neural networks. With batch normalization common issues encountered during training are addressed.
By using stochastic gradient descent technique batch normalization rectifies standardization issues by adjusting the outputs of each layer. This affects the accuracy of the weights in the following layer and improves the training accuracy and overall performance of the neural network model.
By introducing gamma and beta parameters, batch normalization further enhances the representational capacity of the neural model. This allows in scaling and shifting of the normalized inputs. This coordination with gradient descent optimization contributes to reduced data loss and improved network stability during training.
The objective of batch normalization is to make training more stable and help the model perform better on new data. It also reduces the need for careful initial setup of the model's weights and allows for faster training with higher learning rates.
It's typical to use batch normalization before applying the activation function in a layer, and often, it's paired with other techniques like dropout to improve model performance.
Batch normalization is widely used in modern deep learning and has proven effective in tasks such as image classification, natural language processing, and machine translation.
- In a Convolutional Neural Network (CNN), how can you fix the constant validation accuracy?
Constant validation accuracy is the issue in which the Convolutional Neural Network (CNN) model has reached a point where it has stopped learning from the learning data. Here are the steps that can fix this issue:
Data Augmentation: Transformations like rotation, flipping, zooming, and scaling can augment the data by increasing the variability in it. Data Augmentation can also help the model learn more general features.
Adjust the learning rate: By adjusting the learning rate the model's performance can be improved. The model may overshoot the optimal solution if the learning rate is too high or take too much time to reach the optimal solution if the learning rate is too low.
Increase the number of training samples: The model can learn more complex features and patterns if the training samples are increased.
Change the model architecture: Sometimes changing the model architecture is the required solution as the present architecture may limit the model's performance. You can add more layers, change the number of filters in the layers, or change the activation functions.
Regularization techniques: You can also try regularization techniques such as dropout or L2 regularization to improve the model's overall performance.
Learn Deep Learning Concepts with Interview Kickstart
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So, it’s an ideal interview prep course for any deep learning career aspirant.
Here, we have tried to accommodate various deep learning interview questions that serve different sets of learners.
FAQs: Deep Learning Interview Questions
What is the main limitation of deep learning?
One of the main limitations of deep learning is it can only function on large and complex machine learning models. It's an expensive preposition to train such large and complex machine learning models. While training such models extensive hardware is required to perform complex mathematical calculations.
What are the prerequisites to learn deep learning?
The prerequisites to learn deep learning are: strong understanding of math concepts like probability, statistics, linear algebra, and calculus, comprehensive knowledge of data structures and thorough understanding of machine learning concepts.
What are the different types of deep learning models?
Three types of deep learning models are Multi-Layer Perceptrons (MLP), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
- Multi-Layer Perceptrons (MLP): Are fully connected dense layers that transform any input dimension to the desired dimension.
- Convolutional Neural Networks (CNN): Are deep learning algorithms designed specifically for processing structured grid data such as images and graphics.
- Recurrent Neural Networks (RNN): Are artificial neural networks which are designed for processing sequences of data. Through inbuilt connections, RNNs form directed cycles, making it possible for information to persist across time steps.
What are the three steps of deep learning?
There are three processing steps involved in deep learning. These are data understanding and preprocessing, DL model building and training, and validation and interpretation.
How much memory does an average deep learning model takes?
A small neural network can take around a few hundred megabytes (MB) of memory.
Convolutional neural networks (CNNs) used for image classification may require several hundred megabytes to a few gigabytes (GB) of memory.
Natural language processing models like GPT can take up tens of gigabytes (GB) of memory.
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