Neural Network Neural Network Applications and Frameworks 2

Question 1

What is model quantization in the context of deploying neural networks?

Accepted Answer

Reducing numerical precision of weights (e.g., from FP32 to INT8) to decrease model size and inference latency

Answer

Measuring model performance with specific metrics

Answer

Counting the number of parameters in a model

Answer

Converting a model to run on quantum computing hardware

Question 2

What is neural architecture search (NAS)?

Accepted Answer

Automated methods to discover optimal architecture configurations using search algorithms

Answer

Manually designing optimal neural network architectures

Answer

Searching a database of pretrained architectures

Answer

Pruning unnecessary layers from existing architectures

Question 3

What is the purpose of mixed precision training?

Accepted Answer

Using FP16 for most computations while keeping FP32 for critical operations, speeding training while maintaining accuracy

Answer

Using different activation functions for different layers

Answer

Training the model with multiple different loss functions

Answer

Applying different learning rates to different layers

Question 4

What is federated learning in the context of neural network training?

Accepted Answer

Training models across decentralized devices while keeping data local and only sharing model updates

Answer

Distributing training across multiple GPUs in a data center

Answer

A method for federating pretrained models from multiple sources

Answer

Distributed hyperparameter tuning across cloud providers

Question 5

What is model pruning in neural network deployment?

Accepted Answer

Removing weights or neurons that contribute little to predictions, reducing model size

Answer

Removing entire hidden layers from the model

Answer

Deleting misclassified training examples

Answer

Reducing the vocabulary size of NLP models

Question 6

What is the primary use case for TensorFlow Lite?

Accepted Answer

Running pretrained neural network models on mobile and embedded devices with low latency

Answer

Training large models on cloud TPUs

Answer

Visualizing TensorFlow model architectures

Answer

Distributed training across multiple machines