Pytorch Interview Questions (MCQ): Test Your Knowledge!

Which module in PyTorch provides neural network layers?

torch.layer

torch.nn

torch.neural

torch.network

How does torch.distributed.scatter work in PyTorch?

It scatters tensors across processes

For scatter operations in distributed training

To distribute model parameters

To optimize data distribution

How can you use torch.compile in PyTorch 2.0?

To compile models to C++

For ahead-of-time compilation of models

To optimize Python code

To create standalone executables

What are tensors in PyTorch?

Multi-dimensional arrays serving as foundational elements for models

Strings used for text processing

Objects for database management

Functions for mathematical computations

How do you create a tensor with elements drawn from a gamma distribution?

torch.gamma(alpha, beta)

torch.random_gamma(alpha, beta)

torch.dist_gamma(alpha, beta)

torch.gamma_like(alpha, beta)

What is the function of torch.nn.utils.clip_grad_norm_ in PyTorch?

To clip gradients

For gradient norm clipping

To implement adaptive clipping

To optimize training stability

What is the main purpose of data augmentation in image classification?

To increase model complexity

To expand dataset size

To reduce overfitting

To speed up training

What is the main advantage of RNNs over feedforward networks for sequential data?

Faster training

Ability to capture temporal dependencies

Smaller model size

Higher accuracy on all tasks

What is the role of the torch.utils.data.DataLoader class in PyTorch?

To efficiently load data in batches and provide features like shuffling

To define the dataset structure

To perform data preprocessing

To save data to files

What is the function of torch.nn.utils.prune in PyTorch?

To remove unused parameters

To optimize model architecture

To apply pruning techniques to neural networks

To reduce model size after training

Which function is used to concatenate tensors along a given dimension in PyTorch?

torch.concat

torch.cat

torch.join

torch.merge

How can you train a PyTorch model on a distributed cluster?

Using torch.distributed package and DistributedDataParallel

PyTorch doesn't support distributed training

By manually copying the model to each machine

By using a separate distributed computing framework

How does torch.nn.functional.nll_loss work in PyTorch?

It loses NLL values

For computing negative log likelihood loss

To optimize loss calculations

To create custom loss functions

What is the purpose of torch.nn.LSTM in PyTorch?

To implement Long Short-Term Memory networks

To create a loss function

To apply data augmentation

To implement transfer learning

What is the purpose of torch.einsum() in PyTorch?

To perform Einstein summation

To calculate eigenvectors

To compute matrix determinants

To solve linear equations

How does torch.nn.utils.parameters_to_vector work in PyTorch?

It vectorizes parameters

For flattening parameters into a single vector

To implement vector quantization

To optimize parameter storage

Which dataset is used for final model evaluation?

Training dataset

Validation dataset

Test dataset

All of the above

What is the purpose of torch.nn.functional.normalize in PyTorch?

To normalize tensor values

To apply batch normalization

To implement layer normalization

To standardize input features

Which PyTorch module is used for mixed-precision training?

torch.mixed_precision

torch.cuda.amp

torch.fp16

pytorch.amp

What is the purpose of torch.nn.functional.cosine_similarity in PyTorch?

To compute cosine similarity between tensors

To apply cosine activation

To implement cosine annealing

To optimize angular distances

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