Neural Network Neural Network Applications and Frameworks

Question 1

What is PyTorch's dynamic computational graph and how does it differ from TensorFlow 1.x's static graph?

Accepted Answer

PyTorch builds the graph incrementally during execution; TensorFlow 1.x required defining the full graph before running

Answer

PyTorch uses define-by-run (eager execution) building the graph dynamically during forward pass, enabling flexible debugging; TensorFlow 1.x required define-and-run with the full graph declared upfront.

Question 2

What is the purpose of the `nn.Module` class in PyTorch?

Accepted Answer

To serve as the base class for all neural network modules, managing parameters and forward computation

Answer

nn.Module is PyTorch's base class for all neural network components, providing parameter tracking, state dict saving/loading, and a forward() method to define computation.

Question 3

In TensorFlow/Keras, what does model.compile() configure?

Accepted Answer

The optimizer, loss function, and evaluation metrics for training

Answer

model.compile() in Keras sets up the optimizer, loss function, and metrics that will be used during model.fit() training, preparing the model for the training loop.

Question 4

What is ONNX (Open Neural Network Exchange) used for?

Accepted Answer

A standard format for representing deep learning models enabling interoperability between frameworks

Answer

ONNX provides an open format for neural network models, allowing models trained in PyTorch, TensorFlow, or other frameworks to be exported and run in different inference runtimes.

Question 5

What is TensorBoard primarily used for?

Accepted Answer

Visualizing training metrics, model graphs, embeddings, and other diagnostics during training

Answer

TensorBoard is a visualization toolkit for TensorFlow (and PyTorch via SummaryWriter) that displays training curves, model architecture graphs, weight histograms, and embedding projections.

Neural Network Practice Test

Neural Network Practice Test

Neural Network Neural Network Applications and Frameworks