Top Deep Learning Interview Questions and Answers

Deep Learning has almost reshaped what contemporary artificial intelligence involves, especially in the aspects of computer vision, natural language processing, and robotics. The adoption of AI-powered solutions is becoming more popular among Enterprises. In this competitive space, to get ready for interviews, there is a need to have a thorough understanding of the basic principles, and the advanced processes as well.

In this blog, we will cover a wide range of Deep Learning interview questions, from basic queries suitable for freshers to more complex topics aimed at experienced professionals. Whether you are just starting or looking to advance your career, this guide will help you confidently tackle Deep Learning interviews.

Deep Learning Job Trends in 2024

In the year 2024, even in emerging circumstances, the need for trained deep learning experts does not wane because more companies are shifting to AI-powered solutions. Several job opportunities for Deep Learning specialists are being created across sectors including technology, health services, finance, and the automotive industry.

• Increased Demand for AI Skills: Companies have growing opportunities for persons with competent deep learning skills using frameworks such as TensorFlow, Keras and Pytorch.
• Diverse Job Titles: All popular positions that are available include Deep Learning Engineer, AI Research Scientist, and Machine Learning Engineer. These involve creating, employing, or enhancing Deep Learning models to address certain tasks.
• Increase in Availability of Flash Employment: The trends of Deep Learning industries over the last few years include allowing their employees to do their work from anywhere instead of requiring them to come to the office.
• Moving Towards Niche Domains: There is also demand for practitioners focused on narrow areas such as NLP, vision, and reinforcement learning, which are relatively new. Those practitioners are especially appreciated.
• Cross-Cutting Competencies: More and more often, employers need deep learning specialists who are also knowledgeable in other areas, for instance, data science, software engineering, or specific industries such as healthcare, finance, etc.
• AI and Explainability: As AI systems penetrate everyday life, the need for building models that are accurate, comprehensive, as well as ethically considerable has grown over time. Emerging job titles related to AI include ethics, fairness and explainability of outcomes of AI systems.

What is Deep Learning?

Deep Learning can be characterised as a subset of Machine Learning that employs various deep neural networks having multiple layers of non-linear transformations that are used to study complex data patterns. This mimics the way the human brain works, allowing a computer to perform complex tasks with high levels of accuracy, such as recognizing images and speech. This is also the reason why Deep Learning has proved to be very useful in other fields such as computer vision and natural language processing since it deals with a large amount of data that has a complex hierarchy.

What is a neural network?

• Inspired by the human brain’s structure.
• Consists of interconnected nodes (neurons) organised in layers.
• Each neuron processes inputs and passes results to the next layer.
• Fundamental to Deep Learning and used for pattern recognition, data classification, and predictions.

How does Deep Learning differ from Machine Learning?

What are the applications of Deep Learning?

Deep Learning is applied in various sectors because of the ability to study complex data:

• Computer Vision: Image classification, localization, and image segmentation.
• Natural language processing (NLP): Text-to-text translation, opinion mining, dialogue systems.
• Speech Recognition: Transcribing human language, virtual assistants.
• Healthcare: Diagnosis of an ailment, Imaging applications, discovery of drugs.
• Autonomous Cars: Perfect navigation and perception for a self-driving car.

What is a multi-layer perceptron (MLP)?

A multi-layer perceptron (MLP) is defined as a class of fully connected feedforward neural networks which contains an input layer, one or more layers of hidden neurons and an output layer. It is not a one-to-one type structure, as each neuron of an MLP is connected to every neuron in the next layer to extend the network’s functionality in such modelling. The MLP introduces some nonlinear aspects into itself by employing activation functions that make it possible for the MLP to accomplish tasks like classification and regression.

Key Features:

• The input layer receives data.
• Hidden layers process data.
• The output layer generates predictions.

What are the differences between Mean Squared Error and Cross-Entropy Loss?

What is data normalisation, and why do we need it?

Data normalisation is a procedure that modifies the input feature’s values so that they fall in the same range, ideally from 0 to 1 or -1 to 1. This step is very important as it makes sure that each feature carries the same weight in the model and hence no single feature is too powerful since it’s large in scale. In neural networks, normalisation is used to speed up the training process of the networks as well as enhance overall model performance.

Why We Need Normalisation:

• Prevents bias from features with larger scales.
• Speeds up convergence during training.
• Enhances model accuracy by ensuring all features are treated equally.

What is the difference between shallow and deep networks?

What are the challenges in Deep Learning?

• Data Requirements: Deep Learning models require vast amounts of labelled data.
• Computational Power: Training deep models demands significant computational resources, often requiring GPUs.
• Overfitting: Models can easily become too complex, leading to poor generalisation.

What is gradient descent?

Gradient descent is described as an economical optimization approach where the cost function is minimised in very complex neural networks.It works by iteratively adjusting the model’s parameters in the direction of the steepest descent, as indicated by the negative gradient of the cost function. This is done until the optimum of the model is achieved at the very least level where the cost function is low and values generated by the model are at their highest accuracy.

What is the difference between a feedforward neural network and a recurrent neural network?

Why should we use batch normalisation?

Batch normalisation is an approach that helps in training deep neural networks by normalising the output of the previous activation layer. This method also aids in training much faster as it is aimed at reducing the amount of internal covariate shift that is caused by the changes in the distribution of network activations during training.

How to know whether your model is suffering from the problem of exploding gradients?

Exploding gradients happen when the gradient that is taken to update the weights of the neural network increases so much that the model becomes unstable. An observation of these signs will help identify this challenge:

• Extremely large gradient values during training.
• The model’s weights increase uncontrollably.
• The cost function diverges, leading to NaN (Not a Number) errors.
• The training process fails to converge.

Compare Linear Regression and Logistic Regression.

What is a GPU?

A graphics processing unit (GPU) is an external hardware that is mainly used in freeing logic processors from the strain of solving mathematical calculations that would be primarily used in video rendering. On the other hand, Graphics processing units (GPUs), in the field of Deep Learning, can be used in performing tasks which are highly parallel such as training neural networks where there is a significant reduction in training time as opposed to using CPUs.

Advantages of GPUs:

• High parallelism for faster computation.
• Essential for handling large-scale Deep Learning models.
• Reduces training time significantly compared to CPUs.

What is the difference between batch gradient descent and stochastic gradient descent?

What are overfitting and underfitting, and how to combat them?

Overfitting occurs when a model learns the training data too well, including noise and outliers, resulting in poor generalisation to new data. Underfitting happens when a model is too simple to capture the underlying patterns in the data.

Define epoch, iterations, and batches.

• Epoch: One complete pass of the entire dataset through the neural network during training.
• Iterations: The number of times the model’s parameters are updated in one epoch. If the dataset is divided into batches, each batch is an iteration.
• Batches: Subsets of the dataset processed at one time during training. Batching allows for more efficient computation, especially in large datasets.

How are weights initialised in a network?

Weights in a neural network are initialised using various strategies to ensure efficient training:

• Random Initialization: Weights are set to small random values. This breaks the symmetry and allows the network to learn different features.
• Xavier Initialization: Weights are initialised based on the number of input and output neurons. This helps in maintaining the variance of the activations across layers.

Explain the difference between supervised and unsupervised learning.

How do neural networks learn from the data?

Neural networks learn from data through a process called backpropagation. During training:

• The network makes predictions based on the current weights.
• The difference between the predicted and actual outputs is calculated using a loss function.
• Gradients of the loss function concerning each weight are computed.
• The weights are updated using these gradients to minimise the loss.

This process repeats over multiple iterations until the model converges to an optimal set of weights.

What are the different layers on CNN?

• Convolutional Layer: It is a layer that serves to sample features from the input bypassing filters.
• Pooling Layer: This layer serves to downscale the maps (perform feature maps of pictures of much lesser width and height in practice).
• Fully Connected Layer: This layer serves to connect all of the neurons from an earlier layer to the neuron in the current one usually for the purpose of classification.
• Dropout Layer: As with the convolutional layer, this layer also censors a fraction of the input units internally without regard to the real input.

What is overfitting and how to avoid it?

Overfitting indicates when the model has captured the learning data too many times, which includes noise and outliers, leading to poor performance on unseen data.

How to Avoid Overfitting:

• Regularisation: Use L1 or L2 regularisation techniques to avoid big weight values.
• Dropout: Randomly drop neurons during training to prevent co-adaptation.
• Data Augmentation: Increase the diversity of training data by applying transformations.

How does a convolutional neural network (CNN) differ from a recurrent neural network (RNN)?

What exactly is pooling on CNN, and what is its function?

Pooling in a Convolutional Neural Network (CNN) is the act of reducing the feature maps of the image by decreasing its dimensions, height and width without compromising on important features of this mapping.

Different Types of Pooling:

• Max Pooling: Out of the window allocated for pooling, this method picks only the largest one.
• Average Pooling: This method offers an average of all of the values of the window used for pooling.

How It Works: The pooling operation slides a window across the input feature map and applies the pooling function (e.g., max or average) to each region, resulting in a reduced feature map.

Discuss the vanishing gradient in RNN and how it can be solved.

The vanishing gradient problem in Recurrent Neural Networks (RNNs) occurs when gradients become very small during backpropagation through time, causing the network to stop learning effectively. This is especially problematic in deep networks or when processing long sequences.

Compare TensorFlow and PyTorch. How do they differ?

What are the main gates in LSTM and what are their tasks?

• Forget Gate: Decides what information to discard from the cell state.
• Input Gate: Determines what new information to store in the cell state.
• Output Gate: Controls the output from the LSTM cell based on the cell state.

These gates allow the LSTM to maintain long-term dependencies and mitigate the vanishing gradient problem.

Is it a good idea to use CNN to classify 1D signals?

Yes, CNNs can also be used for classifying 1D signals which are traditionally used for classifying 2D image data. It is similarly noteworthy that in 1D signals like time series or sequential data, CNNs can effectively capture local patterns and temporal dependencies.

Benefits of Using CNNs for 1D Signals:

• Efficient Feature Extraction: CNNs can automatically learn important features from the input signals.
• Reduced Complexity: The use of pooling layers reduces the computational load.
• Scalability: CNNs can handle large datasets and complex patterns in 1D signals.

How does gradient descent differ from Newton’s method in optimization?

Explain the key components of a transformer model.

• Multi-Head Attention: Instead of doing one job at a time, this component of the transformer enables the model to direct attention to more than one portion of the input array within the same time frame.
• Positional Encoding: Words must be positioned with respect to many nouns or pronouns since transformers do not possess any arrangement.
• Feedforward Network: A fully connected network applied to each position separately and identically.
• Layer Normalisation: Normalises the input to each sub-layer, improving training stability.
• Residual Connections: Help in training deep networks by allowing gradients to flow through the network.

What is self-attention, and how does it work in transformers?

Self-attention, likewise known as scaled dot-product attention, is an attention mechanism present within elements of a transformer architecture that enables the model to focus on various elements in a sequence as per their relative importance to one another.

How It Works:

• Key, Query, and Value Vectors: For each word in the sequence, a set of key, query, and value vectors is created.
• Attention Scores: The query vector is compared with key vectors of other words to compute attention scores, which determine the relevance of other words.
• Weighted Sum: The final output for each word is a weighted sum of value vectors, where the weights are the attention scores.

Compare classification and regression tasks. What are the key differences?

How does image segmentation work and what are its uses?

Image segmentation can be defined as the splitting of an image into a number of parts such that some change or simplification can be done to the representation of an image. The primary focus is on recognizing the objects and the different boundaries in an image to make the image more useful and easier to interpret.

Applications:

• Medical Imaging: Detecting and locating abnormal tissues such as tumours or organs in the presence of medical images.
• Autonomous Vehicles: Classifying moving objects including but not limited to: People, cars and traffic signals.
• Satellite Image Analysis: Identifying and categorising different types of land use.
• Face Recognition: Separating different components of the face for precisely the reasons of identification.

Define the learning rate in Deep Learning.

As it is a hyperparameter, the learning rate influences the amount of change in the model’s weights made at each update while training. It denotes the speed of learning of a model by controlling the rate of weight adjustment after each epoch/simulation. Any learning ratio that is very large would predict a very rapid convergence which might sometimes lead to getting stuck in a suboptimal point.

Can you explain the steps in acquiring the optimal Deep Learning model for a given task?

Optimising a Deep Learning model involves several techniques and strategies for achieving the best performance:

• Hyperparameter Tuning: Try out different values for parameters that may include learning rate, batch size, and number of layers.
• Regularisation: Methods such as L1/L2 regularisation and Dropout are used to counter the phenomenon of overfitting.
• Gradient Descent Variants: Use of other optimizers for better convergence such as Adam, RMSprop, or SGD with momentum where applicable.
• Data Augmentation: Improve on existing training data set by including image alterations to enhance broad learning.
• Early Stopping: Evaluate the validation data periodically and stop training when further training does not improve performance.

Explain the difference between overfitting and underfitting.

What is a Deep Learning framework?

A Deep Learning framework is a software package or set of instructions that assists in creating, training, and testing Deep Learning algorithms.  These frameworks abstract the complex mathematics involved in neural networks, helping the practitioners to build models without being able to write every piece of code from scratch.

Popular Deep Learning Frameworks:

• TensorFlow: A large universal toolkit created by Google for various machine learning models.
• PyTorch: Developed by Facebook, popular for its dynamic computational graph and ease of use.
• Keras: This is an external software library that uses a high-level neural networks API that runs on top of TensorFlow to design neural network models.
• MXNet: Known for its efficiency and scalability, often used in research and production.

What is gradient clipping?

Gradient clipping is a way used in machine learning to resolve the exploding gradient issues mostly associated with deep networks and RNN architectures. This limits the size of the gradients during the backpropagation process to a certain reasonable level so that they do not go beyond a given threshold.

What are the key differences between sigmoid and tanh activation functions?

Intermediate Deep Learning Interview Questions

How biological neurons are similar to the artificial neural network

Biological neurons and artificial neural networks share several similarities in their functioning and structure:

Neuron Structure:

• Biological Neurons: Consists of dendrites (input), a cell body, and an axon (output).
• Artificial Neurons: Comprise inputs, a processing unit (similar to the cell body), and an output.

Signal Processing:

• Biological Neurons: Receive signals through dendrites, process them, and send an output signal through the axon.
• Artificial Neurons: Receive inputs, apply weights, and an activation function to process the data, and produce an output.

Learning Process:

• Biological Neurons: Strengthen or weaken connections (synapses) based on learning and experience.
• Artificial Neurons: Adjust weights during training to minimise error, similar to strengthening or weakening connections.

What is the Boltzmann Machine?

A Boltzmann Machine is a type of stochastic recurrent neural network that can learn deep representations of data. It consists of two types of units: visible units (input data) and hidden units (latent features). The network learns by adjusting the weights between these units to model the probability distribution of the input data.

Explain the difference between dropout and batch normalisation.

What is the role of activation functions in a neural network?

Activation functions are used in the neural network models to eliminate the linear characteristics within the model. The structure of the neural network is regular linear if there are no activation functions regardless of the number of attics.

What are some of the uses of autoencoders in Deep Learning?

Autoencoders are a type of neural network used for unsupervised learning. It is predominately used in:

• Dimensionality Reduction: Reducing the dimensions of data to a lower volume.
• Denoising: Reduces contingencies in the data by trying to learn how to reverse the input.
• Anomaly Detection: Detecting anomalies based on the principle of difference in data reconstruction.
• Generative Models: Making new data point instances through random sampling of the latent space.

Explain the Adam optimization algorithm.

Adam (Adaptive Moment Estimation) is a method used for the optimization of neural networks and an improvement of two methods (AdaGrad and RMSprop) at the same time. This adjusts for learning of every parameter using the first and second moments of its gradients.

Key Components:

• Learning Rate: Adaptive, allowing the algorithm to work well with sparse gradients.
• Momentum: Incorporates momentum to improve convergence speed.
• Bias Correction: Corrects biases in the moment estimates to improve performance.

How do the AdaGrad and Adam optimizers compare in terms of performance?

Can you name and explain a few hyperparameters used for training a neural network?

• Learning Rate: Controls how much to change the model’s weights with respect to the loss gradient. A careful learning rate guarantees adequate improvements in learning on the average while a risky one guarantees quick results but may many times lead to going past optimum values.
• Batch Size: The number of training examples processed before updating the model’s weights. When small batches are used, the estimated gradients are very accurate and when large batches are used, they speed up the procedure
• Number of epochs: The number of times the model will work on the whole, available data. More epochs allow the model to learn more, but too many can lead to the problem of overfitting.
• Dropout Rate: It refers to the proportion of neurons set to zero during the training process to minimise overfitting. This figure is mostly around 0.5 which means that 50% of the neurons will be dropped.

What is the cross-entropy loss function?

The cross-entropy loss function evaluates the effectiveness of a classifier in which the output is a score or probability between 0 to 1. However, it also helps compare how far the true label is from the expected probability of that event occurring.

Explain a computational graph.

A computational graph is an illustration of the calculations that are performed in machine learning. It shows the instructions as dots and the flow of the information from one instruction to another as links.

What is the difference between softmax and sigmoid functions in neural networks?

What is the difference between training accuracy and validation accuracy?

• Training Accuracy: The performance of the model on the training data set. In other words, it determines how well the model is fitted into the training set whereas such performance is not guaranteed with the test.
• Validation Accuracy: It is the accuracy of the model on a different dataset called the validation dataset which was not made use of during model training. This is a better measure of the future behaviour of the model on unknown data. A large validation accuracy that is significantly worse than the training accuracy is an indication that the model is overfitting.

How does a feedforward neural network differ from a convolutional neural network?

Why is TensorFlow the most preferred library in Deep Learning?

TensorFlow is highly preferred in Deep Learning due to several reasons:

• Scalability: TensorFlow is designed to scale across multiple GPUs and even entire distributed systems, making it ideal for large-scale machine learning tasks.
• Flexibility: It supports a wide range of machine learning algorithms and architectures, from deep neural networks to reinforcement learning models.
• Echosystem: The ecosystem of TensorFlow includes not only Tensorflow Serving to deploy the models, Tensorflow Lite for mobile but also TensorFlow Extended (TFX) end-to-end machine learning systems.

What are the programming elements in TensorFlow?

• Tensors: Multidimensional arrays that are the core data structure in TensorFlow.
• Sessions: Throughout the operations in the computational graph and the evaluation of the tensors a session is utilised.
• Variables: Variables serve as containers and dynamic control updates for parameters that exceed the required number, say, weights in a neural network, during training.

What is the difference between L1 and L2 regularisation techniques?

What are Feedforward Neural Networks?

Feedforward Neural Networks FNN is the most simplistic form of artificial neural networks where there are no cycles in the direction of the flow of information between two nodes. The data is entered through input nodes and fed into output nodes through hidden nodes, if there are any, and progresses in one direction only. No loops or feedback are available.

Advanced Deep Learning Interview Questions for Experienced

What is the cost function?

The cost function also known as the loss function is a function that takes as input a model predictor and the actual dependent variable and gives as output a number. The function is used to assess how far off the estimates of the model are from what they really are, thus aiding in the process of optimization. Therefore for training, the objective of the training process is to minimise the cost function with respect to the parameters of the model.

Examples:

• Mean Squared Error (MSE): Used for regression tasks.
• Cross-Entropy Loss: Commonly used for classification tasks.

What are the softmax and ReLU functions?

Softmax Function:

• Converts a vector of raw scores (logits) into probabilities.
• Used in the output layer of a neural network for multi-class classification.
• The probabilities sum to 1, making it useful for predicting the probability distribution over multiple classes.

ReLU Function (Rectified Linear Unit):

• An activation function that outputs the input directly if it’s positive; otherwise, it outputs zero.
• Introduces non-linearity to the model, allowing it to learn complex patterns.
• Commonly used in hidden layers of deep neural networks.

How do the activation functions ReLU and Leaky ReLU differ?

What is the pooling layer?

In a Convolutional Neural Network, a pooling layer is used to downsample the input feature maps, reduces the depth of the feature maps and reduces the spatial size, length and width of this feature map but does not cause loss of critical content. This layer aids in decreasing the number of calculations required and regulating the excessive fitting.

What is the data augmentation technique in CNNs?

Data augmentation technique is used to increase the size of a training dataset artificially by making modifications in the original data. It aids in enhancing the model since one is able to create variations in the designs of the data as more readings on how it should be done are presented.

Common Augmentation Techniques:

• Flipping: Horizontally or vertically flipping images.
• Rotation: Rotating images by a certain angle.
• Scaling: Zooming in or out on images.
• Translation: Shifting the images horizontally or vertically.
• Colour Jittering: Randomly changing the brightness, contrast, or saturation.

Compare LSTM and GRU. What are the key differences?

Which strategy does not prevent a model from overfitting to the training data?

Increasing Model Complexity: If a model is designed by adding more layers, neurons or both, chances of overfitting will be minimal, as the model will be very complicated and focus on noise in the training data instead of its desired patterns.

How can you train hyperparameters in a neural network?

Hyperparameters in a neural network can be trained or tuned using the following methods:

• Grid Search: Exhaustively searches through a specified subset of hyperparameters.
• Random Search: Samples random combinations of hyperparameters and evaluates performance.
• Bayesian Optimization: Builds a probabilistic model of the objective function and uses it to choose the most promising hyperparameters.

What is object detection, and how does it differ from image classification?

What is LSTM, and how does it work?

LSTM (Long Short Term Memory) is one of the types of recurrent neural networks which is particularly useful for modelling sequences with long-range dependencies. It eliminates the vanishing gradient problem of normal RNN by implementing a cell structure that can store information for a longer period.

How It Works:

The gates control the information, which is important in the sense that it helps the LSTM cell decide whether to keep or erase information after some time. This makes it suitable for the prediction of time series data as well as for processing natural language.

Explain the difference between epoch and batch size in training a neural network.

What is a perceptron?

A perceptron is understood as the most basic type of artificial neural network which is the structural basis of more advanced neural networks. It comprises only two layers: an input layer, and an output layer which is one neuron with no hidden layer.

What is an auto-encoder?

An auto-encoder is a kind of neural network whose goal is to encode and hence efficiently capture the information in the inputs to achieve either dimensionality reduction or trap out noise.

Use Cases:

• Dimensionality Reduction: More often known as feature selection, this technique is focused on preserving key features and reducing the number of features that are available.
• Denoising: This is a commonly used term in the processing of images and it refers to the cleanup of painting.

Compare online learning and batch learning.

Conclusion

Deep Learning interview questions can range from basic concepts like neural networks and activation functions to more advanced topics such as optimization algorithms and model evaluation. Mastering these questions will not only help you in interviews but also deepen your understanding of the field, making you a stronger candidate for roles in AI and machine learning.

As you prepare for interviews, focus on understanding both theoretical concepts and practical applications. This comprehensive knowledge will equip you to answer questions confidently and demonstrate your expertise in Deep Learning.