One of the most important fields of our times, Artificial Intelligence, is also very pervasive. Our lives are captivated by the tools and applications of this lucrative domain. The key sub-field within this domain is Deep Learning. It is heavily dominating the job market.
You wouldn’t need more than a glance through the jobs available to know how much in demand is for skilled professionals who know how to work through the unstructured data in the form of images, texts, audio, and video. You can not step into such a role without preparing yourself, sharpening your arsenal, and having your way worked through real-life cases.
Wouldn’t it be great if we had all the deep learning interview questions in one place? This article will help you precisely with that! We shall provide you with interview questions on deep learning, deep learning python interview questions, the tips for deep learning interview questions and answers, and how you can get expertise in this domain.
Table of Contents
- Top 40 Deep Learning Interview Questions
- Q1. Why do we need Deep Learning when Machine Learning is present?
- Q2. What are the applications of Deep Learning?
- Q3. Name the Deep Learning frameworks and tools that you have used.
- Q4. List the commonly used data structures in deep learning.
- Q5. What are the supervised and unsupervised learning algorithms in Deep Learning?
- Q6. What are deep and shallow networks? Which one is better and why?
- Q7. What is the difference between Single-Layer and Multi-Layer Perceptron?
- Q8. What are the components of the neural network?
- Q9. Explain how a neural network works?
- Q10. What role do weights and bias play in a neural network? How are the weights initialized?
- Q11. What is forward and backward propagation?
- Q12. What is an activation function and why is it needed?
- Q13. Which activation function do we use for each layer?
- Q14. Why can we not use sigmoid or tanh to activate the hidden layer of the neural network?
- Q15. What is the difference between ReLU and LeakyReLU functions?
- Q16. What are the challenges with gradient descent and What are the treatments?
- Q17. What is Adaptive Moment Estimation (or Adam)?
- Q18. What is the vanishing gradient problem? How is it different from the exploding gradient problem?
- Q19. What are the ways to treat the vanishing and exploding gradient problem?
- Q20. What are batch, mini-batch, and stochastic gradient descent? Which one would you use and why?
- Q21. What is data or image augmentation?
- Q22. How do you differentiate between CNN and RNN? Which algorithm should be used when?
- Q23. Why do we use Convolution Neural Network (CNN) for image data and not the Feedforward Neural Network (FNN)?
- Q24. Please explain the difference between Conv1D, Conv2D, and Conv3D?
- Q25. Explain the different layers of CNN. When using CNN, for which all layers are the parameters estimated?
- Q26. In CNN, what is the difference between valid and the same padding?
- Q27. What is the difference between dropout and batch normalization?
- Q28. What is transfer learning? Why is it needed? Name commonly-used transfer learning models.
- Q29. Does the problem of overfitting happen in neural networks? If yes, then how would you treat it?
- Q30. Do we have regularizes for neural networks?
- Q31. What are hyperparameters? List the hyperparameters used in a neural network.
- Q32. Explain how LSTM, and GRU work. Which is the best one to use and why?
- Q33. What are Autoencoders? What is the use of Autoencoders?
- Q34. What are GANs?
- Q35. Explain the Boltzmann machine. What is a Restricted Boltzmann machine?
- Q36. What are Seq2Seq (encoder-decoder) models? How is it different from Autoencoders?
- Q37. What is the difference between epoch, batch, and iteration in deep learning? We have a dataset with 10,000 records and a batch size of 100 then for how many iterations will our model run for?
- Q38. What are tensors?
- Q39. What is a computational graph? How is it useful for Deep Learning?
- Q40. What is model capacity?
- Beginner level Deep Learning Interview Tips
- How to get expertise in Deep learning?
- FAQs – Frequently Asked Questions
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Let’s look at the interview questions on deep learning …
Top 40 Deep Learning Interview Questions
1. Why do we need Deep Learning when Machine Learning is present?
Machine learning is a framework that takes past data to identify the relationships among the features. The machines can predict the new data with the help of mathematical relationships by getting dynamic, accurate, and stable models.
On the other hand, deep learning is a segment of machine learning that uses complex algorithms based on the biological neural network to mimic the human brain and take decisions and actions like a human.
Deep Learning is inspired by the brain’s functioning and structure to train neural networks. Deep learning also performs various complex operations to extract hidden features and patterns.
The critical aspect differentiating the two is that deep learning can extract hidden features and patterns present in the data. In contrast, other machine learning can reduce dimensions but not identify new features.
For more details, please also read Data Science vs. Machine Learning vs. AI Deep Learning – What is the difference?
2. What are the applications of Deep Learning?
The applications of Deep Learning bucketed according to the type of learning are:
The supervised tasks in deep learning are:
- Text Classification
- Sentiment Analysis
- Auto-Tagging System
- Image Classification
- Audio Classification
The unsupervised tasks in deep learning are:
- Image Segmentation
- Image Localization
- Image Captioning
- Object Detection or Identification
The problems that involve generating data in deep learning are:
- Machine Translation
- Speech to Text
- Recommendation Systems
- Automatic Text Generation
You may also like to read: Top 15 Real World Applications of Artificial Intelligence
3. Name the Deep Learning frameworks and tools that you have used.
The deep learning frameworks and tools are:
4. List the commonly used data structures in deep learning.
The data structures used in deep learning are:
- Computation Graphs
5. What are the supervised and unsupervised learning algorithms in Deep Learning?
The supervised learning algorithms are:
- Artificial Neural Network (ANN)
- Perceptron (single and multi-layer)
- Convolution Neural Network (CNN)
- Recurrent Neural Network (RNN)
The unsupervised learning algorithms are:
- Self-Organizing Maps (SOMs)
- Boltzmann Machine
- Generative adversarial networks (GANs)
6. What are deep and shallow networks? Which one is better and why?
A shallow neural network has only one hidden layer, and a deep neural network has more hidden layers.
Both the networks have the ability to approximate any function. A shallow network requires more parameters as is limited by the layers, whereas a deep network can leverage its number of layers to compute efficiently and extract more abstract features.
7. What is the difference between Single-Layer and Multi-Layer Perceptron?
In a neural network, a perceptron is the processing unit that performs computations to extract features. It is the building block of the Artificial Neural Network (ANN).
A single layer perceptron is the simplest neural network without any hidden layer. It is a linear classifier with only one neuron and can perform binary classification. It can only classify the linear separable classes having output as binary.
A multilayer perceptron (MLP) is another class of Artificial Neural Network (ANN) that has more than one perceptron. MLP consists of three layers: input, hidden, and output layer. The input layer receives the information, and the output layer makes the prediction or decision.
There can be one or more hidden layers in an MLP. It can classify the non-linearly separable data points as hidden, and the output layer uses a nonlinear activation function.
8. What are the components of the neural network?
A neural network has:
- Input Layer: This layer consists of the inputs or the independent X variable.
- Hidden Layer: It is the layer that extracts features and helps with fine-tuning the model
- Output Layer: It is the result or the outcome of the network and depends on the type of business problem.
- Weight and Bias
- Activation Function
Both the hidden and the output layer contain nodes.
9. Explain how a neural network works?
There are three steps to perform in any neural network:
- First, take the input variables and estimate the output or the predicted Y values.
- Next, calculate the loss or the error term, and lastly
- Minimize the loss function or the error term by using the backward propagation i.e the loss value is taken backwardly one by one each layer to update the weights in such a way that the error term is minimized.
10. What role do weights and bias play in a neural network? How are the weights initialized?
Ans. It controls the strength of the connections between the neurons and determines the slope coefficients or the betas. Weights or interconnections transform the input data within the hidden layers of the network. The initial values of the weights (or betas) are randomly assigned between 0 and 1.
Bias is the constant intercept term. It is the default value of the model when no inputs or independent variables are present.
11. What is forward and backward propagation?
Forward and backward propagation is the way to adjust the weights or the betas.
In Forward propagation, the weight and biases are randomly initialized. Forward propagation is the process in which the inputs move from the hidden layer to the output layer, i.e., from left to right. In every hidden layer, the activation function’s output is calculated until the other layer is processed.
Backward propagation, go in the reverse order. First, adjust the output layer weights (keeping the weights of the input and hidden layers constant). Relative to the forward propagation, the backward propagation converges faster to the optimal solution of minimized error term since going from the output to the hidden layer and then to the input later.
12. What is an activation function and why is it needed?
Ans. The activation function transforms the inputs into the output by introducing non-linearity to the outcome of a neuron. It helps in deciding to activate or not activate a neuron by calculating the weighted sum with the bias with it.
Activating a neuron will enable the model output to be nonlinear. It is also known as the squashing function. Some of the commonly used activation functions are:
- Step binary function
- Linear or Identity
The objective of the activation function is to add non-linearity to the function.
13. Which activation function do we use for each layer?
Ans. The activation function is only applied to hidden and output layers as the input layer do not have nodes or neurons. We need the outcome in terms of class predicted (1 or 0) on the output layer, so we use sigmoid for binary classification and softmax for multi-classification. For the hidden layer, we do not need the answer in 1 or 0, so use the Rectified linear unit (or ReLU).
14. Why can we not use sigmoid or tanh to activate the hidden layer of the neural network?
The reason for not using Sigmoid or tanh for hidden layers is that these are susceptible to the vanishing gradient problem. By applying to the hidden layers, the gradients get saturated. During the backpropagation process, the error will reduce further (because of the chain rule), and the weights would not get updated, which will lead to no learning, and eventually, the model would not predict.
15. What is the difference between ReLU and LeakyReLU functions?
ReLU is half rectified from the bottom. In ReLU, the function f(x) = 0 when x< 0 and f(x) = x when x >= 0. This way, in ReLU, the gradient is always zero for all the input values which are less than zero. It can lead to the ‘dying ReLU’ problem by deactivating the neurons in that region.
On the other hand, the Leaky ReLU function is f(x) = max(0.001x, x) when x < 0, so it will have a slight positive slope (of 0.01) and, therefore, a non-zero value, and consequently the model continues to learn without hitting a dead-end.
16. What are the challenges with gradient descent and What are the treatments?
The challenges of Gradient Descent are:
- Gradient descent gets stuck at local minima, and
- It has a constant learning rate
The remedies are:
For the first challenge, use another optimizer called stochastic or mini-batch gradient descent with momentum, and for the second challenge, employ an RMSProp optimizer.
Moving out of the local minima requires accumulating momentum or speed. The gradient descent with momentum calculates the exponentially weighted average sum of the previous gradients and uses this weighted sum to update the weights.
RMSProp scales the learning rate depending on the square of the gradients, which eventually leads to faster convergence to the optimal solution.
17. What is Adaptive Moment Estimation (or Adam)?
Adaptive Moment Estimation (or Adam) combines the Stochastic Gradient Descent with Momentum and RMSProp. It can adapt the learning rate and find the exponential weights to the historical derivatives. It gives importance to both the past gradients and the previous learning rates.
18. What is the vanishing gradient problem? How is it different from the exploding gradient problem?
Ans. The purpose of the gradient descent algorithm is to update the weights and biases of the neural network by taking small steps towards the minimum value of the loss function.
The vanishing gradient problem occurs when these steps towards the optimal solution taken are too small, thus leading to gradients disappearing. In other words, the changes in the weights and bias terms are so minimal, almost equivalent to negligible. This way, the model does not learn anything, and the model performance deteriorates.
On the other hand, when the steps are too large, the gradients explode. This enlarges the updates to weights and bias terms, leading to an unstable network.
Both of these problems happen while using the Recurrent Neural Network (RNN) since RNNs use backpropagation through time.
19. What are the ways to treat the vanishing and exploding gradient problem?
To fix the vanishing gradient problem, must use:
- ReLU activation function in the hidden layers instead of tanh and sigmoid, and
- Xavier initialization
To treat the exploding gradients problem can:
- Re-design the network with fewer layers.
- Instead of RNN, use Long Short-Term Memory (LSTM) model
- Use gradient clipping, and
- Use the weight regularization
20. What are batch, mini-batch, and stochastic gradient descent? Which one would you use and why?
In a batch gradient descent:
- At each iteration, the gradient is computed for the entire dataset. It is also known as the vanilla gradient descent.
- The convergence to the optimal solution is slow as the training data is large with many samples and many features.
In Stochastic Gradient Descent (SGD):
- The gradient is estimated for a single observation at each iteration.
- As compared to the batch gradient, SGD converges much faster because it updates the weight more frequently and is therefore efficient for large data.
Mini-Batch Gradient Descent:
- It is the combination of batch gradient descent and SGD. It is also referred to as the vanilla mini-batch gradient descent.
- Here, the gradients are estimated by taking one batch or group of observations (similar to SGD for considering one sample) and then like batch gradient descent, training on this entire batch.
- It works faster than both batch gradient and SGD.
Summarizing the variants of the gradient descents:
Mini-Batch Gradient Descent is the best variant to use because:
- Using the mini-batches improves the convergence, and also avoids the local minima to allow gradient approximation for the entire dataset.
- It is computationally more efficient in comparison to stochastic gradient descent.
- It helps in improving the generalization by finding the flat minima.
21. What is data or image augmentation?
Ans. Data or Image augmentation is a technique for increasing the input data for the model to train and upon. It is done by manipulating the original data. Data augmentation also helps in reducing the overfitting problem.
In image data, an image can be rotated, flipped horizontally or vertically, cropped, converted its channel, added Gaussian blur, scaled, jittering its color, randomly translated, and sheared to create more images used for training.
22. How do you differentiate between CNN and RNN? Which algorithm should be used when?
Convolution Neural Network (CNN) is a type of Feedforward Neural Network (FNN) having convolution as one of its layers. It creates a set of kernels (or filters) that learns and detects features from the unstructured data. CNNs are applicable for performing and analyzing image, signal, and video data. It is used for image recognition, analyzing visual imagery.
In a CNN, the signals travel in uni-direction from the input to output. It takes in only the current input, so it cannot create any network feedback loop and memorize previous inputs.
Recurrent neural network (RNN), a class of artificial neural network, is applied to sequential data to perform complex tasks such as retrieving patterns in the text, time-series data, handwriting, and genomes.
RNNs use Back-propagation Through Time (BTT) to train and store the output of a layer and feed it back to the input layer, enabling it to have an internal state (or memory) for processing the sequences. Unlike CNN, RNN has interconnected neurons in each layer, and the signals travel in both directions that create a looped network.
It is used for image captioning, time series forecasting, recognizing handwriting, chatbots creation, fraud, and anomalies detection.
23. Why do we use Convolutional Neural Network (CNN) for image data and not the Feedforward Neural Network (FNN)?
A convolutional neural network (CNN) is better than a feedforward neural network (FNN) for image data because CNN reads the image in parts rather than taking the entire picture.
CNN has a convolution layer that has filters to build the feature maps. It follows a hierarchical model that creates a network in the shape of a funnel, returns the processed outcome to a densely connected layer in which all the neurons are interconnected, and returns the final image as classified or identified.
This helps in sharing the features parameter, dimensionality reduction and also reduces the computations.
24. Please explain the difference between Conv1D, Conv2D, and Conv3D?
- Conv1D is applicable for input signals which are similar to the voice i.e audio data.
- Conv2D is useful for images, and
- Conv3D is used for videos having a frame for each period.
25. Explain the different layers of CNN. When using CNN, for which all layers are the parameters estimated?
There are four different layers of Convolution Neural Network (CNN):
- Convolutional Layer: The input (images) undergoes a convolution operation. This layer consists of a set of filters (or kernels) which further creates a subset of images, called feature maps. These maps are used to train the neural network.
- ReLu Layer: It adds non-linearity to the network converting the negative pixels to zero. It results in a rectified or corrected feature map.
- Pooling: This layer down-samples and reduces the dimensions of the feature maps. It does so by reducing the spatial size of the representation to a lower number of features in patches of the feature map.
- Fully Connected Layer: In this layer, like a feedforward neural network, all the layers are fully connected meaning the neurons of each layer are connected to neurons of another layer also have complete activations. This layer identifies and classifies the image.
- Flatten layer: It is connected with the fully connected layer. Flatten converts the data into a one-dimensional array to pass as the input for the next layer.
The parameters are estimated for the convolution and fully connected layer, not the flatter and pooling layer.
26. In CNN, what is the difference between valid and the same padding?
Valid padding is used when there is no need for padding. After convolution, the output dimensions of the output matrix are (n – f + 1) X (n – f + 1).
In the Same padding, the elements are added around the edges of the output matrix to keep the dimensions of the input and output matrix the same.
27. What is the difference between dropout and batch normalization?
Ans. Patented by Google, Dropout, and BatchNorm are techniques to improve a deep learning algorithm by reducing the overfitting of the model.
Using the dropout, we can randomly and probabilistically drop or set nodes to zero within layers of a neural network, i.e., effectively making those neurons invisible or “dropped out.” It is a way of creating a different model for each training and averaging the model performance of the network.
The batch normalization technique standardizes the inputs of the network. It normalizes the hidden layer’s activations with a mean of zero and a standard deviation of each layer. This helps reduce the training time by easily initializing the weights and allowing higher learning rates.
28. What is transfer learning? Why is it needed? Name commonly-used transfer learning models.
Transfer learning is the process where learning from a model can be transferred to another without needing to train the model from scratch. There are three ways to use transfer learning:
- Extract features from the pre-trained model and also take away the output from the pre-trained model and then use it on the data for the current problem.
- Use both the architecture and weights of the pre-trained model and take that to train on our dataset.
- Train some layers and freeze the other layers of the pre-trained model. This works by extracting the weights of some of the layers and use for our neural network and fine-tune the model.
The most commonly-used transfer learning models are:
- GTP-2 and GPT-3
29. Does the problem of overfitting happen in neural networks? If yes then how would you treat it?
Ans. Yes, overfitting can occur in a neural network. Following are the ways to prevent overfitting in a neural network or improve a deep learning algorithm:
- Early Stopping
- Batch Normalization
- Data or Image Augmentation
- Weight Sharing
30. Do we have regularizers for neural networks?
Yes, the dropout is a regularizer.
31. What are hyperparameters? List the hyperparameters used in a neural network.
It helps to determine the structure of the neural network and how the network will be trained. Hyperparameters are the variables that are set before the training starts. These are the values that cannot be learned from the data and must be defined by the user. The hyperparameters of an artificial neural network are:
- Number of hidden layers
- Number of Nodes in the hidden layer
- Number of epochs (or iterations)
- Batch size
- Activation Function
- Learning Rate
- Network Weight Initialization
32. Explain how LSTM, and GRU work. Which is the best one to use and why?
Long Short-Term Memory (LSTM) is a variant of the recurrent neural network that can learn long-term dependencies. It uses three gates: forget gate, input, output gate, and standard units to include a ‘memory cell’ that helps maintain the information in memory for long periods. It uses the feedback loop and gates to “remember” & “forget” information.
The LSTM network works in the following manner:
- Step 1: The network picks and decides which information to remember and what to forget.
- Step 2: It selectively updates the cell state values based on the first step.
- Step 3: The network calculates to decide which part of the current state would make it to the output.
Gated Recurrent Unit (GRU) is a special case of LSTM. It uses two gates: a reset gate and an update gate. The reset gate decides how to combine the new input with the previous time steps’ memory. The update gate decides how much of the last memory should be kept. The update gate of GRU is the combination of the input and forget gate of LSTM.
GRU is preferred over LSTM as LSTM can become complex by having an additional gate. GRU also resolves the vanishing gradient problem, operates faster, and takes less memory consumption as GRU does not have internal memory.
33. What are Autoencoders? What is the use of Autoencoders?
Autoencoders is an unsupervised deep learning algorithm. Its goal is to automatically learn to map the inputs to the corresponding outputs without any supervision and direction. It comprises of two parts (which are different from the Encoder-Decoder model):
- Encoder: fits the input into an internal representation
- Decoder: converts the internal computational states back into the output
Autoencoders are primarily used for dimensionality reduction to decrease the size of the inputs into a smaller representation and are also used for image reconstruction, denoising images, and image colorization.
34. What are GANs?
Generative adversarial networks (GANs) are unsupervised algorithms that use two neural networks: one generator and a discriminator. It generates data with the intent to uncover patterns to create the output.
The generator generates new data, and the discriminator classifies the generated data by the generator. Both these parts are trained simultaneously and compete with each other.
GANs are popular with image data as they are highly efficient and have high traction. These are used in image generation, image enhancement, translation, video, voice generation, and age progression.
35. Explain the Boltzmann machine. What is a Restricted Boltzmann machine?
Ans. The Boltzmann Machine resembles a Multilayer Perceptron with a hidden layer and a visible input layer. This model makes binary decisions, and bias, i.e., takes stochastic decisions to decide if a neuron must remain on or off. These generative types of models are bidirectional and stochastic models where nodes across different layers can connect; however, two nodes within the same layer can not connect.
A Restricted Boltzmann Machine (RBM), a variant of the Boltzmann machine. It is an undirected graphical model that can be trained in either supervised or unsupervised ways based on the task. It is an algorithm that is used to perform:
- Collaborative filtering
- Dimensionality reduction
- Feature Learning
- Topic modeling
Restricted Boltzmann Machine is the Boltzmann Machine with restrictions where the restrictions are the visible nodes that should not connect with each other, and the hidden nodes shouldn’t connect with each other.
36. What are Seq2Seq (encoder-decoder) models? How is it different from Autoencoders?
The encoder-decoder model is a recurrent neural network (RNN) architecture with two RNNS and is used for sequence problems such as machine translation. One part is the encoder that encodes the inputs into a fixed-length vector representation. The second part is the decoder that decodes or represents another series of symbols as the output.
It is different from the autoencoder as autoencoder is an unsupervised architecture focussing on reducing dimensions and is applicable for image data.
37. What is the difference between epoch, batch, and iteration in deep learning? We have a dataset with 10,000 records and a batch size of 100 then for how many iterations will our model run for?
Epoch is the number of iterations over which we train the model. Batch is referred to the size of the subsets that the dataset is divided into to pass in the model.
The model will run for 100 iterations ( (10,000 divided by 100) for 1000 records and have a batch of 100.
38. What are tensors?
Tensors are mathematical objects, and in deep learning, tensors are one of the data structures. These multi-dimensional arrays represent data with higher dimensions, and one can execute various mathematical operations on these. In other words, tensors are data container that stores data with different dimensions in a neural network.
To explore more on tensors, read Tensors — Representation of Data In Neural Networks and TensorFlow Basics: Tensor, Shape, Type, Sessions & Operators.
39. What is a computational graph? How is it useful for Deep Learning?
A computational graph is a series of operations represented in a network form, where each node represents a mathematical operation, and the edges are the tensors. Each node takes input as one or more tensors and returns in output a tensor. This sequence of TensorFlow operations is also known as the ‘DataFlow Graph.’
The USP of this graph is processing can happen parallelly hence is computationally efficient. The graph is immensely helpful for deep learning as it takes in a large number of inputs and has multiple layers requiring more computations. It can chart out the mathematical workflow graphically.
40. What is model capacity?
Ans. Model capacity refers to the degree of a deep learning neural network to control the types of mapping functions it can take and learn from. It is the ability to approximate any given function. The higher the model capacity, the more amount of information can be stored in the network.
Beginner level Deep Learning Interview Tips
It would be beneficial to do the following to prepare for the deep learning interview:
- Having good theoretical knowledge
It is imperative to have a sound, solid theoretical knowledge of deep learning topics. Starting from the basics of neural networks, their architecture, the several parameters used in it. A good working understanding of the commonly used models such as Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU).
Be thorough with how the models help solve a business problem, how to improve the neural network and the respective hyperparameters. Additionally, know what transfer learning is, why it is needed and how it is employed. Be well-versed with the fundamentals of the underlying concepts used in the models. Keep revisiting these deep learning interview questions to revise your concepts.
- Work with real-world Deep Learning cases
Nothing beats the old-age saying practice makes a man perfect! All the theory is not of any good if not applied. It is crucial to have worked with the business cases to solve any real-life problems. It is best to showcase your work, skills by having a portfolio on GitHub and/or Kaggle.
- Study Deep Learning interview questions
The above deep learning interview questions and answers would be a good starting point to study. Practice your answers; take mocks. It would save you many rounds of drilling work if you could hear yourself when you answer a question! Technical rounds can get a little daunting at times. In such cases, keep a mind hack trick handy to swift through it.
- Read about the Company and Role
It is an excellent practice to research and read well beforehand the job description, about the company, if possible know its contemporaries. Be aware of the needed skill sets and tools, talk about how you have those skills, and work on cases requiring those tools (only if you have!) Assess and answer yourself on questions such as telling us something about yourself and what you can bring to the role. Have a list of questions to ask the interviewer at the end of the interview.
- Interview Étiquettes
Some of the very subtle and essential things that we can tend to forgo while preparing for interviews are:
- Being punctual for the interviews for both virtual and in-person. Logging in at least a couple of minutes before time will allow you to breathe in, be settled, collect your thoughts and not rush hush through (after all a good start is half done 🙂
- Being honest and integral about what you know and don’t know. On your resume add only the things you know, and have worked on yourself.
- Listen to the interviewer and answer questions to the best of your ability and understanding. Ask in case you are not clear about the question. Answer only to the question asked, sometimes we tend to answer for what was not even asked. If you are not sure or do not know the answer, it is polite to say I don’t know than conjuring up incorrect answers.
How to get expertise in Deep learning?
- Having a Beginner’s Mind
We all want to leverage the benefits of this highly-sort, demanding, and lucrative job and build fantastic working models, but Rome was not built in a day 🙂 It takes work to climb up the ladder and breakthrough this field like any other.
First, work with the basics, get your fundamentals in place and then progress to the advanced topics in deep learning such as transformers, machine translations, speech-to-text, applied computer vision, BERT, YOLO.
- Do not forget your Machine Learning Stuff!
If you were expecting that you could let your Machine Learning books, notes, models catch dust, then sorry to disappoint, but you can’t proceed with deep Learning until you have Machine Learning under your belt!
Deep Learning without Machine learning would be like you have become a doctor without having had science in 12th class! (No, no, Munna-Bhai MBBS works here!) There is a reason why Deep Learning is a subset of Machine Learning, and it has to be the last one to be learned and mastered. The pecking order of things is important.
- Pick your Deep Learning framework
There are three popular frameworks to work with deep learning problems: Keras, TensorFlow, and PyTorch. Keras is easier to start working and implementing the Deep Learning models upon as it has pre-defined functions as sklearn had for Machine Learning. Keras has TensorFlow at its backend.
PyTorch requires more work, as we need to define functions to the same task that is handier in Keras. Ideally, start with Keras, and once you have a grasp of the models, clear on the architecture et al. and then learn PyTorch and diverse out.
- Practice Practice and some more Practice!
It would be good to have intuition built for neural networks, the logical understanding of things, how the pieces are joined together. The curiosity to ask questions, understand, learn and then apply all that knowledge gained is the key. It is one thing to understand the architecture of the network, but only once you start putting that into solving real-life cases will you be able to learn more and fine-tune your approach.
- Explore the problems and datasets
Play around with every kind of dataset you can get your hands-on, and over a period of time, you will learn which area of Deep Learning you have flair and liking for. This can only come up with having worked with lots of problems and diversifying the use cases. It is exactly like how you get to know yourself 🙂
- Learn Model Deployment
Eventually, the models you are making have to be deployed for them to be of use. Therefore, it is of immense benefit to learn how to deploy your models. The skill will not only give you added advantage but also make you more efficient.
Hope this repository of deep learning interview questions has been helpful to you. Some of the FAQs related to it are:
For hands-on AI projects, also refer to 18 (Interesting) Artificial Intelligence Projects Ideas
FAQs – Frequently Asked Questions
Ques 1. What are the topics in Deep Learning?
Ans. The topics in Deep Learning are:
- Basics of Neural Networks
- Multi-Layer Perceptron
- Forward and Backward Propagation
- Gradient Descent Algorithm
- Loss Function
- Activation Functions
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long-Short Term Memory (LSTM)
- Gated Recurrent Unit (GRU)
- Attention Models
- Restricted Boltzmann Machine (RBM)
- Speech-to-Text Analytics
- Image, Text, and Audio Processing
- Computer Vision
- Transfer Learning
- Techniques to improve the Deep Learning model
- Deep Learning Frameworks such as Keras, TensorFlow, PyTorch
Ques 2. How do you prepare for a Deep Learning interview?
Ans. To prepare for a deep learning interview, you must have a very sound theoretical knowledge of the basics of neural networks, the terminologies involved in it, and the different algorithms used covered in the topics of deep learning above. Go over the deep learning interview questions.
You must also have an end-to-end working knowledge of algorithms themselves. Be thorough with a deep learning project’s workflow and be ready to walk through your best or favorite deep learning use case during the interview. Having a repository to showcase your work and a portfolio on Github or Kaggle, or both is an added advantage.
Also, study for the deep learning python interview questions. Implementing the business cases on different frameworks of deep learning such as Keras and Pytorch is a must-know. It is important to know the use, functionalities of the modules, methods available within these packages.
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