Practice
1.
import torch
x = torch.tensor([-1.0, 0.0, 1.0])
output = torch.nn.functional.relu(x)
print(output)easy
Solution
Step 1: Understand ReLU behavior.
ReLU outputs zero for negative inputs and the input itself for non-negative inputs.Step 2: Apply ReLU to each element.
For -1.0, output is 0.0; for 0.0, output is 0.0; for 1.0, output is 1.0.Final Answer:
Option A -> Option AQuick Check:
ReLU zeros negatives, keeps positives. [OK]
Hint: ReLU zeros negatives, keeps positives. [OK]
Common Mistakes:
2. In a deep learning model, when is it more appropriate to use Layer Normalization instead of Batch Normalization?
easy
Solution
Step 1: Understand the context of normalization techniques.
Batch Normalization relies on batch statistics, which can be unstable with small batch sizes. Layer Normalization uses layer statistics, making it more stable for small batches or online learning.Step 2: Eliminate wrong options.
When the model is trained on a very large batch size. is incorrect because large batch sizes are suitable for Batch Normalization. When the model has a very deep architecture. is incorrect because depth doesn't directly affect the choice between these normalizations. When the model is trained on a balanced dataset. is irrelevant to normalization choice.Final Answer:
Option B -> Option BQuick Check:
Layer Norm for small batches or online learning. [OK]
Hint: Layer Norm for small batches or online learning. [OK]
Common Mistakes:
3. When framing a business problem as an ML problem, which trade-off is most critical to consider?
medium
Solution
Step 1: Identify the key trade-off
In many business contexts, the trade-off between accuracy and interpretability is crucial because stakeholders need to understand model decisions.Step 2: Eliminate wrong options
Data volume vs. Model complexity is incorrect because data volume and model complexity are often complementary. Training time vs. Inference time is incorrect because training and inference times are usually secondary to model performance. Feature engineering vs. Hyperparameter tuning is incorrect because feature engineering and hyperparameter tuning are not directly opposing trade-offs.Final Answer:
Option C -> Option CQuick Check:
Balance accuracy with interpretability for stakeholder trust. [OK]
Hint: Balance accuracy with interpretability for stakeholder trust. [OK]
Common Mistakes:
4. A KNN model is performing poorly on a high-dimensional dataset. What is the most likely cause?
medium
Solution
Step 1: Consider high-dimensional effects
In high dimensions, distance metrics often become less effective.Step 2: Eliminate wrong options
The model has too few neighbors. is incorrect as increasing neighbors may not help. The dataset is too small. is wrong because size isn't the main issue. The model is overfitting due to too many neighbors. is incorrect as overfitting is less likely with many neighbors.Final Answer:
Option B -> Option BQuick Check:
High dimensions dilute distance metrics. [OK]
Hint: High dimensions dilute distance metrics. [OK]
Common Mistakes:
5. You implemented stratified sampling but notice that the sample still has class imbalance. What is the most likely cause?
medium
Solution
Step 1: Identify the stratified sampling requirement.
Stratified sampling requires correct class proportion specification.Step 2: Eliminate unrelated issues.
Random Sampling (B) would not be used if stratified was intended. Data leakage (C) affects model performance, not sampling. Sample size (D) affects variability, not imbalance directly.Final Answer:
Option C -> Option CQuick Check:
Check class proportions in stratified sampling [[check]]
Hint: Check class proportions in stratified sampling [[check]]
Common Mistakes: