✅ Correct answer: A) Reduces spatial dimensions to 1x1 while preserving channel depth

from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, GlobalAveragePooling2D, Dense

model = Sequential([
    Conv2D(32, (3,3), activation='relu', input_shape=(64,64,3)),
    # ... more conv layers ...
    GlobalAveragePooling2D(),  # Reduces HxW to 1x1
    Dense(10, activation='softmax')  # Classification layer
])

### Key Advantages:
1. Parameter Efficiency: Eliminates need for flattening + dense layers
2. Translation Invariance: Summarizes spatial information
3. Regularization Effect: Reduces overfitting vs. dense layers

### Comparison:
- Without GAP: Flatten() → Dense(256) → Dense(10) (200K+ params)
- With GAP: Direct to Dense(10) (~500 params)

*Common Use Cases:*
- Lightweight mobile models (MobileNet)
- Feature extraction for transfer learning

✅ Correct answer: C) `cv2.Sobel()` with dx=1, dy=1

import cv2
import numpy as np

img = cv2.imread('image.jpg', cv2.IMREAD_GRAYSCALE)

# Sobel with directional gradients
sobel_x = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=5)  # Horizontal edges
sobel_y = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=5)  # Vertical edges
combined = np.sqrt(sobel_x**2 + sobel_y**2)  # Magnitude preserves direction

### Key Characteristics:
1. Sobel:
- Outputs gradient magnitude and direction (via dx/dy)
- Kernel size (ksize) controls sensitivity
- Use cv2.CV_64F to handle negative gradients

2. Alternatives:
- Laplacian: No directionality (2nd derivative)
- Canny: Directional but non-linear (hysteresis thresholding)
- blur: Loses edges

### Practical Tip:

# Visualize edge directions
angles = np.arctan2(sobel_y, sobel_x)  # -π to π radians
hsv = np.zeros((*img.shape, 3), dtype=np.uint8)
hsv[..., 0] = (angles + np.pi) * 90/np.pi  # Hue = direction
hsv[..., 2] = cv2.normalize(combined, None, 0, 255, cv2.NORM_MINMAX)  # Value = magnitude
direction_map = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

✅ Correct answer: B) O(n) - Linear time (shifts all elements)

import timeit

# Benchmark demonstration
def test_insert(n):
    lst = list(range(n))
    start = timeit.default_timer()
    lst.insert(0, -1)  # Insert at beginning
    return timeit.default_timer() - start

sizes = [10**3, 10**4, 10**5]
times = [test_insert(n) for n in sizes]
print(times)  # Times increase linearly with n

### Key Insights:
1. Memory Layout: Python lists are contiguous arrays
2. Insert at 0: Requires shifting all existing elements right
3. Append vs Insert:
- lst.append(): O(1) amortized
- lst.insert(0): Always O(n)

### Performance Comparison:

# Alternative O(1) options for frequent front-insertions:
from collections import deque
d = deque()
d.appendleft(1)  # O(1) operation

*Use Case Guide*:
- Lists: Best for back-heavy operations
- Deque: Preferred for queue-like operations (FIFO)

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Question 30 (Intermediate - PyTorch):
What is the purpose of torch.no_grad() context manager in PyTorch?

A) Disables model training
B) Speeds up computations by disabling gradient tracking
C) Forces GPU memory cleanup
D) Enables distributed training

#Python #PyTorch #DeepLearning #NeuralNetworks

✅ By: https://t.iss.one/DataScienceQ

Question 31 (Intermediate - Django ORM):
When using Django ORM's select_related() and prefetch_related() for query optimization, which statement is correct?

A) select_related uses JOINs (1 SQL query) while prefetch_related uses 2+ queries
B) Both methods generate exactly one SQL query
C) prefetch_related works only with ForeignKey relationships
D) select_related is better for many-to-many relationships

#Python #Django #ORM #Database

✅ By: https://t.iss.one/DataScienceQ

Question 32 (Advanced - NLP & RNNs):
What is the key limitation of vanilla RNNs for NLP tasks that led to the development of LSTMs and GRUs?

A) Vanishing gradients in long sequences
B) High GPU memory usage
C) Inability to handle embeddings
D) Single-direction processing only

#Python #NLP #RNN #DeepLearning

✅ By: https://t.iss.one/DataScienceQ

✅ Correct answer: B) Speeds up computations by disabling gradient tracking

import torch

model = torch.nn.Linear(10, 1)
x = torch.randn(5, 10)

# Inference without gradient tracking
with torch.no_grad():
    prediction = model(x)  # 30-50% faster than regular forward()
    print(prediction.requires_grad)  # False

### Key Use Cases:
1. Model Inference:
- Reduces memory overhead by ~40%
- Prevents accidental weight updates

2. Validation/Testing:

   for data in val_loader:
       with torch.no_grad():
           outputs = model(data)  # No backprop needed
   

3. Weight Freezing:

   for param in model.layer.parameters():
       param.requires_grad = False  # Often used with no_grad()
   

### Performance Impact:
| Operation | Time (ms) | Memory (MB) |
|--------------------|-----------|-------------|
| Regular Forward | 15.2 | 1200 |
| no_grad() Forward| 9.8 | 720 |

*Note: Critical for deployment where every millisecond matters*

✅ Correct answer: A) `select_related` uses JOINs (1 SQL query) while `prefetch_related` uses 2+ queries

# Example models
class Author(models.Model):
    name = models.CharField(max_length=100)

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.ForeignKey(Author, on_delete=models.CASCADE)
    genres = models.ManyToManyField('Genre')

# Optimized queries
books = Book.objects.select_related('author')  # Single JOIN query
books = Book.objects.prefetch_related('genres')  # 2 queries: books + genres

### Key Differences:
| Method | SQL Queries | Best For | Underlying Mechanism |
|----------------------|-------------|------------------------|----------------------|
| select_related() | 1 | ForeignKey, OneToOne | SQL JOIN |
| prefetch_related() | 2+ | ManyToMany, Reverse FK | Python-level caching |

### Performance Benchmark:

# Without optimization (N+1 problem)
for book in Book.objects.all():
    print(book.author.name)  # 1 query per book!

# With select_related (1 query total)
for book in Book.objects.select_related('author').all():
    print(book.author.name)  # Data already loaded

*Pro Tip*: Use Django Debug Toolbar to verify query counts!

✅ Correct answer: A) Vanishing gradients in long sequences

# Vanilla RNN vs LSTM comparison
import torch.nn as nn

rnn = nn.RNN(input_size=100, hidden_size=50, num_layers=1)
lstm = nn.LSTM(input_size=100, hidden_size=50, num_layers=1)

# Forward pass for 10 timesteps
inputs = torch.randn(10, 1, 100)  # (seq_len, batch, input_size)
h_rnn = torch.zeros(1, 1, 50)     # Initial hidden state
h_lstm = (torch.zeros(1, 1, 50), torch.zeros(1, 1, 50))  # LSTM state

out_rnn, _ = rnn(inputs, h_rnn)     # Prone to vanishing gradients
out_lstm, _ = lstm(inputs, h_lstm)  # Better long-term memory

### Key Problems with Vanilla RNNs:
1. Gradient Issues:
- Error signals decay exponentially over timesteps
- Tanh/Sigmoid activations compound the problem

2. LSTM/GRU Solutions:
| Mechanism | Purpose |
|-----------------|----------------------------------|
| Forget Gate | Controls what to remember |
| Input Gate | Regulates new information |
| Cell State | Highway for long-term gradients |

### Practical Impact:

# Training a sentiment analyzer
rnn_model = nn.RNN(embed_dim, hidden_dim)       # Fails beyond 50 words  
lstm_model = nn.LSTM(embed_dim, hidden_dim)     # Handles 500+ words  

*Modern Alternative*: Transformers (no recurrent connections at all)

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