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Master Your ML & DSML Interview
2103 Curated Machine Learning, Data Science, Python & LLMs Interview Questions
Answered To Get Your Next Six-Figure Job Offer
Top 30 AWS Machine Learning Interview Questions  AWS Machine Learning  30  
Top 47 Anomaly Detection Interview Questions  Anomaly Detection  47  
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Top 13 Autoencoders Interview Questions  Autoencoders  13  
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Top 19 Bias & Variance Interview Questions  Bias & Variance  19  
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👨‍💻 Having Full-Stack & Coding Interview? Check  FullStack.Cafe - 3877 Full-Stack, Coding & System Design Questions and AnswersHaving Full-Stack & Coding Interview? Check 👨‍💻 FullStack.Cafe - 3877 Full-Stack, Coding & System Design Questions and Answers

Top 36 PyTorch Interview Questions

Entry Junior Mid Senior Expert
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PyTorch Theoretical Questions
Q1:   

Name some common PyTorch modules

  
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Q2:   

What is PyTorch is used for?

  
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Q3:   

What are some methods to reshape the tensor dimensions in PyTorch?

  
Add to PDF   Junior 
Q4:   

Briefly compare PyTorch vs TensorFlow

  Related To: TensorFlow
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Q5:   

Name some different components of PyTorch

  
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Q6:   

What are Tensors in PyTorch?

  
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Q7:   

What are the different ways to perform Matrix Multiplication in PyTorch?

  
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Q8:   

How can you obtain the derivatives of a function with PyTorch?

  
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Q9:   

What's the difference between the forward() and backward() methods in PyTorch?

  Related To: Neural Networks
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Q10:   

How to define a Neural Network model in PyTorch?

  Related To: Neural Networks
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Q11:   

What are the most common errors you may face when working with PyTorch and how would you solve them?

  
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Q12:   

What are the fundamental steps for a training loop in PyTorch?

  
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Q13:   

What are some good practices to increase reproducibility when working with PyTorch?

  
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Q14:   

What is the use of torch.no_grad in PyTorch?

  
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Q15:   

What are the different ways you can include or exclude a tensor from the computational graph in PyTorch?

  
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Q16:   

Why would you use tensor hooks in PyTorch?

  
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Q17:   

How can you freeze some selected layers of a model in PyTorch?

  
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Q18:   

How do you implement a custom loss function in PyTorch?

  
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Q19:   

What does model.train() and model.eval() do in a PyTorch model?

  
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Q20:   

What is Dynamic Computation Graphs and how are they used in PyTorch?

  
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Q21:   

How to set up PyTorch for being used in a GPU?

  
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Q22:   

What's the meaning of the required_grad attribute in PyTorch? When to use it?

  
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Q23:   

In PyTorch, why would you need to call optimizer.zero_grad()?

  
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Q24:   

How can you add a custom PyTorch transformation function into a pipeline?

  
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Q25:   

How can you change the Learning Rate during training in PyTorch?

  
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Q26:   

How to implement Distributed Training in PyTorch?

  
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Q27:   

What is the connection between loss.backward() and optimizer.step() in PyTorch?

  
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Q28:   

How Hooks can be implemented on PyTorch tensors?

  
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Q29:   

What's the differences between leaf and non-leaf variables in the context of a computational graph in PyTorch?

  Related To: Neural Networks
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Q30:   

What happens when calling model(input) vs model.forward(input)?

  
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Q31:   

How can you implement a custom layer function in PyTorch?

  
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Q32:   

Can you share some best practices when developing a model with PyTorch?

  
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Q33:   

How to iterate through all the dataset when training a model with PyTorch?

  Related To: Neural Networks
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Q34:   

How to initialise Weight and Bias in PyTorch?

  Related To: Bias & Variance
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Q35:   

How can you use a pre-trained model for fine-tuning in PyTorch?

  
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Q36:   

Why would you define a backward method for a custom layer in PyTorch?

  Related To: Neural Networks
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Top 30 AWS Machine Learning Interview Questions  AWS Machine Learning  30  

Amazone runs the internet as we know it. Amazon Web Services (AWS) offers a comprehensive suite of machine learning (ML) services that cater to various needs and expertise levels. Follow along and learn the 23 most common AWS machine-learning intervi...

Top 30 Azure ML Interview Questions  Azure ML  30  

Azure Machine Learning (Azure ML) is a cloud-based service for creating and managing machine learning solutions. It’s designed to scale, distribute, and deploy machine learning models to the cloud. Follow along and learn the 23 most common Azure Mach...

Top 32 Hadoop Interview Questions  Hadoop  32  

Hadoop is an open-source big data processing framework. It leverages distributed computing to store and process large datasets in a fault-tolerant manner. According to recent reports, Apache Hadoop is one of the most sought-after big data skills with...

23 Apache Spark Interview Questions (ANSWERED) To Learn Before ML & Big Data Interview
Apache Spark is a unified analytics engine for large-scale data processing. It is built to handle various use cases in big data analytics, including data processing, machine learning, and graph processing. Follow along and learn the 23 most common an...
Top 30 Apache Spark Interview Questions  Apache Spark  30  
23 Scala Interview Questions (ANSWERED) For Your Next Data Science Interview
Scala is a powerful language with functional programming capabilities that can be a good choice for data science, especially in big data and distributed computing scenarios. As an example, Apache Spark, a popular distributed data processing framework...
Top 39 Scala Interview Questions  Scala  39  
30 PyTorch Interview Questions (ANSWERED) To Beat Your Next Machine Learning Interview
PyTorch popularity as a Deep Learning framework of choice is on the rise. As of December 2022, 62% of the academic papers were implemented in PyTorch whereas only 4% were for TensorFlow. Follow along and prepare effectively with these key 30 PyTorch ...
Top 36 PyTorch Interview Questions  PyTorch  36  
23 Common Artificial Intelligence Interview Questions (SOLVED) For Your Next ML & DS Interview
The use of Artificial Intelligence (AI) in machine learning and data science enabled advancements in areas such as natural language processing, computer vision, recommendation systems, fraud detection, predictive analytics, and personalized medicine....
Top 42 ChatGPT Interview Questions  ChatGPT  42  
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Top 140 Neural Networks Interview Questions  Neural Networks  140  
21 Data Science Optimisation Algorithms Interview Questions (ANSWERED)
Optimization algorithms are extensively used in training machine learning models. Data engineers employ algorithms like gradient descent, stochastic gradient descent, and variants (e.g., Adam, RMSprop) to optimize the model parameters and minimize th...
Top 33 Optimisation Interview Questions  Optimisation  33  
27 ChatGPT Interview Questions (ANSWERED) For ML and Prompt Engineers
ChatGPT, an implementation of the GPT (Generative Pre-trained Transformer) model excels in understanding and generating human-like text, making it a powerful tool for NLP tasks. ML engineers and software developers can leverage ChatGPT's capabilities...
Top 42 ChatGPT Interview Questions  ChatGPT  42  
27 Large Language Models (LLMs) Interview Questions (ANSWERED) For your Next ML and GPT Interview
Large Language Models (LLMs), such as GPT-3.5, have revolutionized natural language processing by demonstrating the ability to generate human-like text and comprehend context. Follow along to understand the top 27 LLMs-related interview questions and...
Top 42 ChatGPT Interview Questions  ChatGPT  42  
Top 59 LLMs Interview Questions  LLMs  59  

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