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2103 Curated Machine Learning, Data Science, AI & LLMs Interview Questions
Answered To Get Your Next Six-Figure Job Offer
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Top 28 Gradient Descent Interview Questions

Entry Junior Mid Senior Expert
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Gradient Descent Theoretical Questions
Q1:   

What is the difference between Cost Function vs Gradient Descent?

  Related To: Cost Function
Add to PDF   Junior 
Q2:   

What is the idea behind the Gradient Descent?

  
Add to PDF   Junior 
Q3:   

Can Gradient Descent be applied to Non-Convex Functions?

  
Add to PDF   Junior 
Q4:   

What is the difference between Maximum Likelihood Estimation and Gradient Descent?

  
Add to PDF   Junior 
Q5:   

Explain the intuition behind Gradient Descent algorithm

  Related To: Linear Regression
Add to PDF   Junior 
Q6:   

What are some types of Gradient Descent do you know?

  
 Add to PDF   Mid 
Q7:   

How do Gradient-Based Algorithms deal with the flat regions with desired points?

  
 Add to PDF   Mid 
Q8:   

What are some necessary Mathematical Properties a Loss Function needs to have in Gradient-Based Optimization?

  Related To: Cost Function
 Add to PDF   Mid 
Q9:   

When should we use Algorithms like Adam as opposed to SGD?

  Related To: Optimisation
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Q10:   

How does Batch Size affect the Convergence of SGD and why?

  Related To: Optimisation
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Q11:   

When Optimizing a Neural Network, how do you define the Termination Condition for Gradient Descent?

  Related To: Optimisation
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Q12:   

How does the Adam method of Stochastic Gradient Descent work?

  Related To: Optimisation
 Add to PDF   Mid 
Q13:   

Compare Batch Gradient Descent and Stochastic Gradient Descent

  Related To: Optimisation
 Add to PDF   Mid 
Q14:   

In which case you would use Gradient Descent method or Ordinary Least Squares and why?

  Related To: Linear Regression
 Add to PDF   Mid 
Q15:   

Name some Evaluation Metrics for Regression Model and when you would use one?

  Related To: Linear Regression
 Add to PDF   Mid 
Q16:   

Explain how does the Gradient descent work in Linear Regression

  Related To: Linear Regression
 Add to PDF   Mid 
Q17:   

Compare the Mini-batch Gradient Descent, Stochastic Gradient Descent, and Batch Gradient Descent

  
 Add to PDF   Mid 
Q18:   

What is the difference between Momentum based Gradient Descent and Nesterov's Accelerated Gradient Descent?

  Related To: Optimisation
 Add to PDF   Senior 
Q19:   

Explain in detail how Momentum-based Gradient Descent and Nesterov's Accelerated Gradient Descent work

  Related To: Optimisation
 Add to PDF   Senior 
Q20:   

Name some advantages of using Gradient descent vs Ordinary Least Squares for Linear Regression

  Related To: Linear Regression
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Q21:   

In what situations would you prefer Coordinate Descent over Gradient Descent?

  Related To: Optimisation
 Add to PDF   Senior 
Q22:   

For both Convex, and Non-Convex Problems, does the Gradient in SGD always point to the global extreme value?

  Related To: Optimisation
 Add to PDF   Senior 
Q23:   

Explain Mathematically, how Stochastic Gradient Descent saves time compared to Standard Gradient Descent.

  
 Add to PDF   Senior 
Q24:   

How is the Adam Optimization Algorithm different when compared to Stochastic Gradient Descent?

  
 Add to PDF   Senior 
Q25:   

What is the Mirror Descent?

  Related To: Optimisation
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Q26:   

Does Gradient Descent always converge to an optimum?

  
 Add to PDF   Senior 
Q27:   

What is the difference between Gradient Descent and Stochastic Gradient Descent?

  
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Gradient Descent Practical Challenges
Q1:   

How would you train Linear Regression model using Gradient Descent? Implement in Python

 PY Related To: Linear Regression, Python
  Add to PDF   Senior 
 
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