Neural Network Neural Network Applications and Frameworks 3

Question 1

What is a recommendation system using neural networks and how does it differ from traditional collaborative filtering?

Accepted Answer

It uses deep embeddings to learn complex user-item interactions beyond linear patterns in traditional matrix factorization

Answer

Neural collaborative filtering learns non-linear user-item interactions through deep embeddings and MLP layers, capturing complex patterns that traditional dot-product-based methods miss.

Question 2

What is a Siamese network and what tasks is it used for?

Accepted Answer

Two identical networks sharing weights that learn similarity between input pairs, used for verification tasks

Answer

Siamese networks use two weight-sharing branches to encode two inputs into embeddings, measuring their similarity or distance for tasks like face verification, signature matching, or one-shot learning.

Question 3

What is the primary challenge of applying neural networks to tabular data compared to images?

Accepted Answer

Tabular data lacks the spatial structure that makes CNNs effective, and gradient boosting often outperforms deep learning

Answer

Unlike images, tabular data lacks spatial/sequential structure, and tree-based methods like XGBoost often outperform neural networks; recent architectures like TabNet and FT-Transformer aim to close this gap.

Question 4

What is a graph neural network (GNN) and what type of data is it designed for?

Accepted Answer

A neural network that operates on graph-structured data by aggregating information from neighboring nodes

Answer

GNNs process graph-structured data (nodes and edges) by iteratively aggregating and transforming features from neighboring nodes, learning representations that capture graph topology.

Question 5

What is the purpose of hooks in PyTorch and when are they used?

Accepted Answer

To register functions that execute on forward/backward passes for tasks like feature extraction or gradient analysis

Answer

PyTorch hooks (forward hooks and backward hooks) let you register callback functions on modules or tensors to inspect or modify activations and gradients during computation, useful for debugging and feature visualization.

Neural Network Practice Test

Neural Network Practice Test

Neural Network Neural Network Applications and Frameworks 3