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LLM Interview Questions.pdf
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Top 50 LLM Interview Questions!
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Topic: 25 Important RNN (Recurrent Neural Networks) Interview Questions with Answers
---
1. What is an RNN?
An RNN is a neural network designed to handle sequential data by maintaining a hidden state that captures information about previous elements in the sequence.
---
2. How does an RNN differ from a traditional feedforward neural network?
RNNs have loops allowing information to persist, while feedforward networks process inputs independently without memory.
---
3. What is the vanishing gradient problem in RNNs?
It occurs when gradients become too small during backpropagation, making it difficult to learn long-term dependencies.
---
4. How is the hidden state in an RNN updated?
The hidden state is updated at each time step using the current input and the previous hidden state.
---
5. What are common applications of RNNs?
Text generation, machine translation, speech recognition, sentiment analysis, and time-series forecasting.
---
6. What are the limitations of vanilla RNNs?
They struggle with long sequences due to vanishing gradients and cannot effectively capture long-term dependencies.
---
7. What is an LSTM?
A type of RNN designed to remember long-term dependencies using memory cells and gates.
---
8. What is a GRU?
A Gated Recurrent Unit is a simplified version of LSTM with fewer gates, making it faster and more efficient.
---
9. What are the components of an LSTM?
Forget gate, input gate, output gate, and cell state.
---
10. What is a bidirectional RNN?
An RNN that processes input in both forward and backward directions to capture context from both ends.
---
11. What is teacher forcing in RNN training?
It’s a training technique where the actual output is passed as the next input during training, improving convergence.
---
12. What is a sequence-to-sequence model?
A model consisting of an encoder and decoder RNN used for tasks like translation and summarization.
---
13. What is attention in RNNs?
A mechanism that helps the model focus on relevant parts of the input sequence when generating output.
---
14. What is gradient clipping and why is it used?
It's a technique to prevent exploding gradients by limiting the gradient values during backpropagation.
---
15. What’s the difference between using the final hidden state vs. all hidden states?
Final hidden state is used for classification, while all hidden states are used for sequence generation tasks.
---
16. How do you handle variable-length sequences in RNNs?
By padding sequences to equal length and optionally using packed sequences in frameworks like PyTorch.
---
17. What is the role of the hidden size in an RNN?
It determines the dimensionality of the hidden state vector and affects model capacity.
---
18. How do you prevent overfitting in RNNs?
Using dropout, early stopping, regularization, and data augmentation.
---
19. Can RNNs be used for real-time predictions?
Yes, especially GRUs due to their efficiency and lower latency.
---
20. What is the time complexity of an RNN?
It is generally O(T × H²), where T is sequence length and H is hidden size.
---
21. What are packed sequences in PyTorch?
A way to efficiently process variable-length sequences without wasting computation on padding.
---
22. How does backpropagation through time (BPTT) work?
It’s a variant of backpropagation used to train RNNs by unrolling the network through time steps.
---
23. Can RNNs process non-sequential data?
While possible, they are not optimal for non-sequential tasks; CNNs or FFNs are better suited.
---
24. What’s the impact of increasing sequence length in RNNs?
It makes training harder due to vanishing gradients and higher memory usage.
---
25. When would you choose LSTM over GRU?
When long-term dependency modeling is critical and training time is less of a concern.
---
#RNN #LSTM #GRU #DeepLearning #InterviewQuestions
https://t.iss.one/DataScienceM
---
1. What is an RNN?
An RNN is a neural network designed to handle sequential data by maintaining a hidden state that captures information about previous elements in the sequence.
---
2. How does an RNN differ from a traditional feedforward neural network?
RNNs have loops allowing information to persist, while feedforward networks process inputs independently without memory.
---
3. What is the vanishing gradient problem in RNNs?
It occurs when gradients become too small during backpropagation, making it difficult to learn long-term dependencies.
---
4. How is the hidden state in an RNN updated?
The hidden state is updated at each time step using the current input and the previous hidden state.
---
5. What are common applications of RNNs?
Text generation, machine translation, speech recognition, sentiment analysis, and time-series forecasting.
---
6. What are the limitations of vanilla RNNs?
They struggle with long sequences due to vanishing gradients and cannot effectively capture long-term dependencies.
---
7. What is an LSTM?
A type of RNN designed to remember long-term dependencies using memory cells and gates.
---
8. What is a GRU?
A Gated Recurrent Unit is a simplified version of LSTM with fewer gates, making it faster and more efficient.
---
9. What are the components of an LSTM?
Forget gate, input gate, output gate, and cell state.
---
10. What is a bidirectional RNN?
An RNN that processes input in both forward and backward directions to capture context from both ends.
---
11. What is teacher forcing in RNN training?
It’s a training technique where the actual output is passed as the next input during training, improving convergence.
---
12. What is a sequence-to-sequence model?
A model consisting of an encoder and decoder RNN used for tasks like translation and summarization.
---
13. What is attention in RNNs?
A mechanism that helps the model focus on relevant parts of the input sequence when generating output.
---
14. What is gradient clipping and why is it used?
It's a technique to prevent exploding gradients by limiting the gradient values during backpropagation.
---
15. What’s the difference between using the final hidden state vs. all hidden states?
Final hidden state is used for classification, while all hidden states are used for sequence generation tasks.
---
16. How do you handle variable-length sequences in RNNs?
By padding sequences to equal length and optionally using packed sequences in frameworks like PyTorch.
---
17. What is the role of the hidden size in an RNN?
It determines the dimensionality of the hidden state vector and affects model capacity.
---
18. How do you prevent overfitting in RNNs?
Using dropout, early stopping, regularization, and data augmentation.
---
19. Can RNNs be used for real-time predictions?
Yes, especially GRUs due to their efficiency and lower latency.
---
20. What is the time complexity of an RNN?
It is generally O(T × H²), where T is sequence length and H is hidden size.
---
21. What are packed sequences in PyTorch?
A way to efficiently process variable-length sequences without wasting computation on padding.
---
22. How does backpropagation through time (BPTT) work?
It’s a variant of backpropagation used to train RNNs by unrolling the network through time steps.
---
23. Can RNNs process non-sequential data?
While possible, they are not optimal for non-sequential tasks; CNNs or FFNs are better suited.
---
24. What’s the impact of increasing sequence length in RNNs?
It makes training harder due to vanishing gradients and higher memory usage.
---
25. When would you choose LSTM over GRU?
When long-term dependency modeling is critical and training time is less of a concern.
---
#RNN #LSTM #GRU #DeepLearning #InterviewQuestions
https://t.iss.one/DataScienceM
❤4
Topic: Python Matplotlib – Important 20 Interview Questions with Answers
---
### 1. What is Matplotlib in Python?
Answer:
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is highly customizable and works well with NumPy and pandas.
---
### 2. What is the difference between `plt.plot()` and `plt.scatter()`?
Answer:
•
•
---
### 3. How do you add a title and axis labels to a plot?
Answer:
---
### 4. How can you create multiple subplots in one figure?
Answer:
Use
---
### 5. How do you save a plot to a file?
Answer:
---
### 6. What is the role of `plt.show()`?
Answer:
It displays the figure window containing the plot. Required for interactive sessions or scripts.
---
### 7. What is a histogram in Matplotlib?
Answer:
A histogram is used to visualize the frequency distribution of numeric data using
---
### 8. What does `plt.figure(figsize=(8,6))` do?
Answer:
It creates a new figure with a specified width and height (in inches).
---
### 9. How do you add a legend to your plot?
Answer:
You must specify
---
### 10. What are some common `cmap` (color map) options?
Answer:
---
### 11. How do you create a bar chart?
Answer:
---
### 12. How can you rotate x-axis tick labels?
Answer:
---
### 13. How do you add a grid to the plot?
Answer:
---
### 14. What is the difference between `imshow()` and `matshow()`?
Answer:
•
•
---
### 15. How do you change the style of a plot globally?
Answer:
---
### 16. How can you add annotations to specific data points?
Answer:
---
### 17. How do you create a pie chart in Matplotlib?
Answer:
---
### 18. How do you plot a heatmap in Matplotlib?
Answer:
---
### 19. Can Matplotlib create 3D plots?
Answer:
Yes. Use:
Then:
---
### 20. How do you add error bars to your data?
Answer:
---
### Exercise
Choose 5 of the above functions and implement a mini-dashboard with line, bar, and pie plots in one figure layout.
---
#Python #Matplotlib #InterviewQuestions #DataVisualization #TechInterview
https://t.iss.one/DataScienceM
---
### 1. What is Matplotlib in Python?
Answer:
Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. It is highly customizable and works well with NumPy and pandas.
---
### 2. What is the difference between `plt.plot()` and `plt.scatter()`?
Answer:
•
plt.plot() is used for line plots.•
plt.scatter() is used for creating scatter (dot) plots.---
### 3. How do you add a title and axis labels to a plot?
Answer:
plt.title("My Plot")
plt.xlabel("X-axis")
plt.ylabel("Y-axis")---
### 4. How can you create multiple subplots in one figure?
Answer:
Use
plt.subplots() to create a grid layout of subplots.fig, axs = plt.subplots(2, 2)
---
### 5. How do you save a plot to a file?
Answer:
plt.savefig("myplot.png", dpi=300)---
### 6. What is the role of `plt.show()`?
Answer:
It displays the figure window containing the plot. Required for interactive sessions or scripts.
---
### 7. What is a histogram in Matplotlib?
Answer:
A histogram is used to visualize the frequency distribution of numeric data using
plt.hist().---
### 8. What does `plt.figure(figsize=(8,6))` do?
Answer:
It creates a new figure with a specified width and height (in inches).
---
### 9. How do you add a legend to your plot?
Answer:
plt.legend()
You must specify
label='something' in your plot function.---
### 10. What are some common `cmap` (color map) options?
Answer:
'viridis', 'plasma', 'hot', 'coolwarm', 'gray', 'jet', etc.---
### 11. How do you create a bar chart?
Answer:
plt.bar(categories, values)
---
### 12. How can you rotate x-axis tick labels?
Answer:
plt.xticks(rotation=45)
---
### 13. How do you add a grid to the plot?
Answer:
plt.grid(True)
---
### 14. What is the difference between `imshow()` and `matshow()`?
Answer:
•
imshow() is general-purpose for image data.•
matshow() is optimized for 2D matrices and auto-configures the axes.---
### 15. How do you change the style of a plot globally?
Answer:
plt.style.use('ggplot')---
### 16. How can you add annotations to specific data points?
Answer:
plt.annotate('label', xy=(x, y), xytext=(x+1, y+1), arrowprops=dict(arrowstyle='->'))---
### 17. How do you create a pie chart in Matplotlib?
Answer:
plt.pie(data, labels=labels, autopct='%1.1f%%')
---
### 18. How do you plot a heatmap in Matplotlib?
Answer:
plt.imshow(matrix, cmap='hot')
plt.colorbar()
---
### 19. Can Matplotlib create 3D plots?
Answer:
Yes. Use:
from mpl_toolkits.mplot3d import Axes3D
Then:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
---
### 20. How do you add error bars to your data?
Answer:
plt.errorbar(x, y, yerr=errors, fmt='o')
---
### Exercise
Choose 5 of the above functions and implement a mini-dashboard with line, bar, and pie plots in one figure layout.
---
#Python #Matplotlib #InterviewQuestions #DataVisualization #TechInterview
https://t.iss.one/DataScienceM
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