Top 30 Artificial Intelligence Interview Questions and Answers
Ever since we realized how AI positively impacts the market, almost every large enterprise has been looking for AI professionals to help them realize their vision.
Ever since we realized how AI positively impacts the market, almost every large enterprise has been looking for AI professionals to help them realize their vision.
In this article on AI interview questions, we will see the 30 most common AI interview questions. So for your better understanding, this includes all AI interview questions for beginners and beginners who want to get an AI job.
1. What methods are used for dimensionality reduction?
Dimensionality reduction is the technique of reducing the number of random variables. We can minimize dimensionality using missing value ratio, low variance filter, high correlation filter, random forest, principal component analysis, etc.
2. List the various methods for supervised sequential learning.
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Sliding window methods
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Iterative sliding window methods
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Hidden Markov models
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Maximum entropy Markov models
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Conditional random fields
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Networks of graph transformers
3. What are the benefits of neural networks?
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Require less formal statistical training
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Have the ability to detect non-linear relationships between variables
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Catch all probable dealings between the predictor variables
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Availability of multiple training algorithms
4. What is the bias and variance trade-off?
The bias error measures how much the predicted values ??differ from the actual values ??on average. If a significant error occurs, we have an underperforming model.
The variance measures how different the predictions made on the same observation are. A model with high variance will overcrowd the data set and perform poorly on any observation.
5. What is TensorFlow?
TensorFlow is an open-source platform for machine learning. It is a quick, flexible, and low-level toolkit for complicated algorithms and offers users the customizability to create experimental learning architectures and work on them to produce desired outputs.
6. What are TensorFlow objects?
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Constants
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Variables
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Placeholder symbol
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Chart
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Meeting
7. What is a cost function?
The cost function is a scalar function that quantifies the error element of the neural network. Lower costs work better for neural networks. For example, when classifying an image in the MNIST dataset, the input image is the digit 2, but the neural network incorrectly predicts it as 3.
8. List the different activating neurons or functions.
9. What are ANN hyperparameters?
Learning rate: Learning rate is how quickly the grid memorizes its parameters.
Momentum: It is a parameter that assists in getting out of local lows and smoothing out jumps during descent.
Multiple Epochs: The number of times all the training data was fed to the network during training is directed as the number of epochs. We raise the number of epochs until the validation accuracy decreases even as the training accuracy increases (overfitting).
10. What is a vanishing gradient?
As we add more hidden layers, backpropagation becomes less helpful in passing information to lower layers. As the data is fed back, the gradients begin to fade and decrease relative to the network weights.
11. What is Dropout?
Dropout is a simple way to avoid overloading a neural network. It is about the failure of some units in the neural network. This is similar to the natural reproductive process, where nature produces offspring by combining different genes (dropping others) rather than reinforcing their common adaptation.
12. Define LSTM.
Long short-term memory (LSTM) is specifically designed to address the long-term dependence problem by keeping a state of what to memorize and what to forget.
13. List the critical components of LSTM.
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Gates (forget, Memory, update and Read)
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Tanh(x) (values ??between -1 and 1)
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Sigmoid(x) (values ??between 0 and 1)
14. List the variants of RNN.
15. What is an autoencoder? Name a few applications.
The autoencoder basically serves to learn the compressed form of the given data. Below are some autoencoder applications:
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Data denoising
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Dimension reduction
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Image reconstruction
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Coloring the image
16. What are the components of a generative adversarial network (GAN)? How do you deploy it?
GAN Components:
Deployment steps:
17. What steps are involved in the gradient descent algorithm?
Gradient descent is an optimization algorithm that finds parameter coefficients that minimize the cost function.
Step 1: Assign weights (x,y) to random values ??and calculate error (SSE)
Step 2: Compute the gradient, i.e., the deviation in SSE when the consequences (x,y)
change by a minimum value. This helps us shift the x and y values ??in the direction in which the SSE is minimized.
Step 3: Adjust weights using gradients to move towards optimal values ??where SSE is minimized
Step 4: Use the new weights to predict and compute the new SSE
Step 5: Repeat steps 2 and 3 until additional adjustments to the consequences significantly reduce the error.
18. What are mean tensors? Do sessions have lifetimes?
Intermediate tensors are neither inputs nor outputs of a Session.run () call but are in the path from information to results; they will be freed at or before the end of the circle.
Sessions can own resources and several classes, such as tf.Variable, tf.QueueBase, and tf.ReaderBase, and use a significant amount of memory. These resources (and the associated memory) are freed when the session is closed by calling tf.Session.close.
19. What is the lifetime of a variable?
When we first run the tf.Variable.initializer operation on a variable in a session; it is initialized. It gets destroyed when we run the tf.Session.close operation.
20. Is it possible to solve logical inference in propositional logic?
Yes, logical inference can be easily solved in propositional logic by using three concepts:
21. How does facial verification work?
Facial verification is used by many popular businesses today. Facebook is known for using DeepFace for facial authentication needs.
There are four main items to think about when understanding how facial verification works:
Input: Scan an image or group of images
Process:
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Facial feature detection
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Feature comparison and alignment
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Key pattern representation
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Final classification of images
Output: A face representation that is the result of a multi-layer neural network
Training Data: Includes the use of thousands of millions of images
Implementing face authentication in Python requires special libraries such as glob, NumPy, OpenCV(cv2), and face_recognition. OpenCV is one of the most broadly used computer vision and image processing libraries.
OpenCV is a beginner-friendly cross-platform python library mainly used for real-time image and video processing applications. You can create applications for object detection, face recognition, and object tracking with OpenCV. It can also extract facial features and identify unique patterns for face verification.
22. What are the components of relational assessment techniques?
23. What is regularization in machine learning?
Regularization comes into play when the model is either overfit or underfit. It is basically used to minimize the errors in the data set. New information is inserted into the dataset to avoid fitting problems.
24. What are model accuracy and model performance?
Model accuracy, a subpart of model performance, is based on the algorithm’s model performance. While the model’s performance is based on the datasets, we supply them as inputs to the algorithm.
25. Define F1 score.
The F1 score is a weighted average of precision and recall. It takes into account both false positives and false negatives. It is used to estimate the performance of the model.
26. Can you name three feature selection techniques in machine learning?
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One dimensional selection
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The meaning of the function
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A correlation matrix with the heat map
27. What is a referral system?
A recommender system is a data filtering system that predicts user preferences based on the choice patterns that the user follows when browsing/using the system.
28. What are some algorithms used for hyperparameter optimization?
Many algorithms are used to optimize hyperparameters, and the following are three main ones that are widely used:
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Bayesian optimization
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Search in the grid
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Random Search
29. What is excess equipment? How is surplus equipment repaired?
Overfitting is a case that appears in statistical modeling or machine learning when an algorithm begins to overanalyze the data, resulting in a lot of noise rather than helpful information. This causes low distortion but high dispersion, which is not favorable.
Overfitting can be prevented using the methods below:
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Early stop
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File models
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Cross-validation
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Removing a feature
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Regulation
30. How is overfitting avoided in neural networks?
In neural networks, overfitting is avoided using a regularization technique called “dropout.”
Using the concept of dropouts, random neurons are dropped when the neural network is trained to use a model that does not overlap. If the dropout value is too small, it will have the tiniest effect. If it is too large, the model will have a problem learning.
Final thoughts
Interview questions are crucial to preparing for the dream role you’ve always wanted. We hope this article has assisted you in being aware of the 30 most common AI interview questions and their relevant answers.
