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Top 31 TensorFlow Interview Questions

Entry Junior Mid Senior Expert
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TensorFlow Theoretical Questions

Q1:   

Briefly compare PyTorch vs TensorFlow

  Related To: PyTorch
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Q2:   

What is the default method of variable initialisation in tf.get_variable()?

  
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Q3:   

In the context of merging tensors, what's the difference between tf.concat and tf.stack?

  
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Q4:   

What is the difference between Dataset.from_tensors and Dataset.from_tensor_slices and when would you use each one?

  
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Q5:   

How would you save an entire model in tensorflow.keras?

  
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Q6:   

Why would you bucket a feature and how can you do it with TensorFlow?

  
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Q7:   

What does this code do?

  
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Q8:   

How would you get the gradient of the loss at a TensorFlow variable?

  
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Q9:   

How can you use a preprocessing layer in a TensorFlow/Keras model?

  Related To: Keras
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Q10:   

What is the advantage of using a tensorflow.data.Dataset over a regular tensorflow.Tensor for a dataset?

  
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Q11:   

What is Feature Cross and how can you create them with TensorFlow?

  
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Q12:   

What do the TensorFlow Dataset's functions cache() and prefetch() do?

  
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Q13:   

What's the difference of name_scope and variable_scope in TensorFlow?

  
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Q14:   

What does batch, repeat, and shuffle do with TensorFlow Dataset?

  
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Q15:   

What's the difference between tf.Variable and tf.get_variable?

  
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Q16:   

What are the common problems that we may face when reading remote data? How can you overcome it with TensorFlow?

  
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Q17:   

What's the difference between tf.clip_by_value, tf.clip_by_global_norm and tf.clip_by_norm and when would you use each one?

  
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Q18:   

Compare Eager vs Graph Execution in TensorFlow

  
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Q19:   

What is the purpose of tf.GradientTape() in Eager Execution?

  
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Q20:   

How Neural Style Transfer does it work? How do you compute the losses?

  
 Add to PDF   Expert 
Q21:   

How to apply Gradient Clipping in TensorFlow?

  
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TensorFlow Practical Challenges

Q1:   

How to obtain the value of a Tensor object in TensorFlow?

  
 Add to PDF   Entry 
Q2:   

Compute the batch inner product in TensorFlow

  
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Q3:   

Using TensorFlow, get unique elements and their indices from x array.

  
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Q4:   

Construct the required data pipeline with TensorFlow

  
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Q5:   

Complete the code below to construct and fit the required model, then visualize the final architecture and learning process using TensorBoard

  
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Q6:   

Compute the gradients using TensorFlow

  
  Add to PDF   Mid 
Q7:   

Complete the code below to construct and evaluate a Convolutional Network in TensorFlow

  Related To: Neural Networks
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Q8:   

Convert the Python function to an equivalent graph in TensorFlow

  
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Q9:   

How to add regularisations in TensorFlow?

  
  Add to PDF   Senior 
Q10:   

Tensor N in the shape of (a, b, c) challenge

  
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