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Artificial Intelligence Interview Questions & Answers- Part 4
LISTEN TO THE AI FAQs LIKE AN AUDIOBOOK
Artificial Intelligence is everywhere, from apps on your phone to robots in factories. If you’re getting ready for a tech interview, this page is here to help you succeed. We’ve gathered common AI questions that employers often ask, paired with simple, clear answers to make studying easier.
You’ll find topics like AI Fundamentals, Machine Learning Algorithms, Deep Learning, Natural Language Processing, and more. Whether you’re just starting out or want to practice advanced AI concepts, this page is perfect for building your knowledge.
The guide is based on the questions most commonly asked in a real interview, so you can practice and feel prepared. Check out the questions and enhance your AI knowledge.
Answer:
Ensemble learning is a computational approach that combines classifiers or experts in a strategic manner to enhance the performance of a model. It is utilized to improve various aspects such as classification, prediction, and function approximation.
Answer:
Following are the steps involve in Machine Learning process:
- Data collection
- Data preparation
- Selection of an appropriate model
- Training the dataset
- Evaluation
- Parameter tuning
- Making predictions
Answer:
A hash table is a data structure used to create an associative array, commonly employed for efficient database indexing.
Answer:
Following are the components of relational evaluation techniques:
- Data acquisition
- Ground truth acquisition
- Cross-validation technique
- Query type
- Scoring metric
- Significance test
Answer:
Model accuracy is a subset of model performance and is determined by the algorithm’s performance. Model performance, on the other hand, is evaluated based on the datasets provided as inputs to the algorithm.
Answer:
F1 score is a measure of a model’s performance and represents the weighted average of precision and recall. It takes into consideration both false positive and false negative values.
Answer:
- Image, speech, and face detection
- Bioinformatics
- Market segmentation
- Manufacturing and inventory management
- Fraud detection, among others
Answer:
Three techniques for feature selection in Machine Learning are as follow:
- Univariate Selection
- Feature Importance
- Correlation Matrix with Heatmap
Answer:
A recommendation system, an information filtering system, is designed to predict user preferences based on their browsing or system usage patterns.
Answer:
Dimensionality reduction refers to the process of reducing the number of random variables. Techniques such as missing values ratio, low variance filter, high correlation filter, random forest, and principal component analysis (PCA) can be employed to diminish dimensionality.
Answer:
- Sliding window methods
- Recurrent sliding window methods
- Hidden Markov models
- Maximum entropy Markov models
- Conditional random fields
- Graph transformer networks
Answer:
The Bias-Variance tradeoff refers to the connection between the bias error and variance in a predictive model. Bias measures the average deviation of predicted values from the actual values, while variance quantifies the variability of predictions for the same observation. A high-bias model underperforms, while a high-variance model overfits the data.
Answer:
TensorFlow is an open-source Machine Learning library that provides a flexible and efficient platform to build complex algorithms. It is a low-level toolkit that allows users to create and customize experimental learning architectures to achieve desired outcomes.
Answer:
It’s essential to have “Python” installed on your machine in order to install TensorFlow. The recommended Python version to start with when installing TensorFlow is 3.4 or later.
To install TensorFlow in Windows, take into account the steps below.
Step 1 − Verify the installed python version.
Step 2 − To install TensorFlow in the system, a user can choose any method. Both “pip” and “Anaconda” are suggested. Python modules can be installed and run with the command pip.
We first need to set up the Anaconda framework on our machine before installing TensorFlow.
Check in the command prompt with the “conda” command after the installation has been successful.
Step 3− Run the following command to start the TensorFlow installation.
conda create –name tensorflow python = 3.5
It downloads the necessary packages needed for TensorFlow setup.
Step 4 − It is crucial to turn on the TensorFlow module after a successful environmental setup.
activate tensorflow
Step 5 − To install “Tensorflow” on the system, use pip. The installation command is listed below. pip install tensorflow
And,
pip install tensorflow-gpu
Answer:
- Constants
- Variables
- Placeholder
- Graph
- Session
Answer:
A cost function is a scalar function used to quantify the error or loss of a neural network. It measures the discrepancy between predicted outputs and actual outputs. A lower value of the cost function indicates better performance of the neural network. For example, if the input image in the MNIST dataset is a digit 2 but the neural network predicts it as a 3, the cost function will be high.
Answer:
- Linear neuron
- Binary threshold neuron
- Stochastic binary neuron
- Sigmoid neuron
- Tanh function
- Rectified linear unit (ReLU)
Answer:
- Learning rate: Determines how quickly the network learns its parameters.
- Momentum: A parameter that aids in escaping local minima and smoothing the gradient descent process.
- Number of epochs: The number of times the entire training dataset is passed through the network during training. Increasing the number of epochs may lead to overfitting if the validation accuracy starts to decrease while the training accuracy increases.
Answer:
Dropout is a technique used to prevent overfitting in neural networks. It involves randomly deactivating a percentage of units or neurons in a network during training. This encourages the network to rely on multiple pathways and reduces co-adaptation, similar to the natural reproduction process where distinct genes are combined in offspring.
Answer:
LSTM, or Long Short-Term Memory, is a type of recurrent neural network (RNN) architecture that addresses the challenge of capturing long-term dependencies in sequential data. It uses a memory cell and various gates to selectively remember or forget information, allowing it to retain relevant information over long periods while filtering out noise.