Machine Learning

1. What is the main numerical reason batch normalization accelerates training? A. It increases model capacity B. It reduces internal covariate shift C. It removes the need for regularization D. It replaces activation functions Correct answer: B. 2. Why are sigmoid activations problematic in deep networks? A. They are non-differentiable B. They produce sparse activations C. They saturate and cause vanishing gradients D. They require large learning rates Correct answer: C. 3. What happens when the learning rate is set too high? A. Training converges slowly B. The model overfits C. The loss oscillates or diverges D. Gradients vanish Correct answer: C. 4. In convolutional layers, what determines the receptive field size? A. Number of filters B. Kernel size and depth C. Activation function D. Optimizer type Correct answer: B. 5. Why is weight sharing important in CNNs? A. It increases model depth B. It reduces computational cost and parameters C. It improves gradient descent accuracy D. It prevents exploding gradients Correct answer: B. 6. What is the primary function of padding in convolutional networks? A. Increase number of channels B. Reduce overfitting C. Preserve spatial dimensions D. Normalize input values Correct answer: C. 7. Which condition most strongly indicates data leakage? A. High training accuracy B. Low training loss C. Validation performance better than training D. Slow convergence Correct answer: C. 8. Why are recurrent neural networks difficult to train on long sequences? A. High memory usage B. Nonlinear activations C. Vanishing and exploding gradients D. Large batch sizes Correct answer: C. 9. What architectural feature allows LSTMs to mitigate vanishing gradients? A. Residual connections B. Gated cell state C. Dropout layers D. Weight decay Correct answer: B. 10. In sequence modeling, what does teacher forcing refer to? A. Using larger batch sizes B. Feeding ground-truth outputs during training C. Freezing embedding layers D. Shuffling time steps Correct answer: B. 11. Why is softmax unsuitable for multi-label classification? A. It is not differentiable B. It enforces mutually exclusive class probabilities C. It cannot handle sparse targets D. It causes gradient explosion Correct answer: B. 12. What does L2 regularization mathematically penalize? A. Absolute values of weights B. Squared magnitude of weights C. Number of parameters D. Gradient variance Correct answer: B. 13. Why does mean squared error perform poorly for classification? A. It is computationally expensive B. It ignores class imbalance C. It provides weak gradients for confident wrong predictions D. It cannot be minimized Correct answer: C. 14. What is the main advantage of global average pooling? A. Increases spatial resolution B. Adds trainable parameters C. Reduces overfitting by eliminating dense layers D. Improves gradient flow Correct answer: C. 15. Why are pretrained embeddings useful in NLP tasks? A. They reduce input sequence length B. They encode semantic relationships learned from large corpora C. They eliminate the need for tokenization D. They prevent overfitting entirely Correct answer: B. 16. What does gradient clipping primarily prevent? A. Overfitting B. Vanishing gradients C. Exploding gradients D. Data leakage Correct answer: C. 17. Why is shuffling training data between epochs important? A. To increase batch size B. To improve memory usage C. To reduce bias in gradient updates D. To stabilize validation loss Correct answer: C. 18. What is the main risk of excessive model capacity? A. Slow inference B. Underfitting C. Overfitting D. Numerical instability Correct answer: C.

1. What is the primary purpose of a loss function in a neural network? A. To initialize model weights B. To measure the model’s prediction error C. To update training data D. To visualize model performance Correct answer: B. 2. Which component is responsible for updating model weights during training? A. Loss function B. Activation function C. Optimizer D. Metric Correct answer: C. 3. What does an epoch represent during model training? A. A single weight update B. One forward pass only C. One complete pass over the training dataset D. One mini-batch Correct answer: C. 4. Which activation function is commonly used in hidden layers to mitigate vanishing gradients? A. Sigmoid B. Tanh C. ReLU D. Softmax Correct answer: C. 5. What is the main role of the validation dataset? A. To update model weights B. To test final model performance C. To tune hyperparameters and monitor overfitting D. To normalize input data Correct answer: C. 6. Which technique randomly disables neurons during training to reduce overfitting? A. Batch normalization B. Dropout C. Data augmentation D. Early stopping Correct answer: B. 7. What problem does regularization primarily address? A. Underfitting B. Exploding gradients C. Overfitting D. Data leakage Correct answer: C. 8. Which type of neural network is best suited for image data? A. Recurrent Neural Network B. Fully Connected Network C. Convolutional Neural Network D. Autoencoder Correct answer: C. 9. What is the purpose of convolutional filters in CNNs? A. To reduce dataset size B. To detect local patterns in data C. To normalize pixel values D. To perform classification directly Correct answer: B. 10. What does pooling primarily achieve in convolutional neural networks? A. Increases spatial resolution B. Reduces overfitting by adding noise C. Reduces spatial dimensions and computation D. Converts images to vectors Correct answer: C. 11. Which loss function is most appropriate for multi-class classification? A. Mean Squared Error B. Binary Crossentropy C. Categorical Crossentropy D. Hinge Loss Correct answer: C. 12. What is a common symptom of overfitting? A. High training loss and high validation loss B. Low training loss and high validation loss C. High training accuracy and low training loss D. Low training accuracy and low validation accuracy Correct answer: B. 13. What does backpropagation compute? A. Model predictions B. Loss values only C. Gradients of the loss with respect to weights D. Input feature scaling Correct answer: C. 14. Which Keras method is used to define the training configuration of a model? A. fit() B. compile() C. evaluate() D. predict() Correct answer: B. 15. What is transfer learning primarily based on? A. Training from scratch on small datasets B. Reusing pre-trained models or layers C. Random weight initialization D. Increasing model depth Correct answer: B. 16. Which type of layer is used to flatten multidimensional input into a vector? A. Dense B. Conv2D C. Flatten D. Dropout Correct answer: C. 17. What is the main advantage of mini-batch gradient descent? A. Exact gradient computation B. No memory usage C. Faster convergence with stable updates D. Eliminates need for an optimizer Correct answer: C. 18. Which metric is commonly used to evaluate classification models? A. Mean Absolute Error B. R-squared C. Accuracy D. Perplexity Correct answer: C. 19. What is the primary goal of early stopping? A. Speed up data loading B. Prevent overfitting by stopping training at the right time C. Increase model capacity D. Improve gradient flow Correct answer: B. 20. Which framework is primarily used in the book to implement deep learning models? A. PyTorch B. Scikit-learn C. Keras with TensorFlow backend D. MXNet Correct answer: C. https://t.me/DataScienceM

1. What is the primary goal of a neural network? A. Store data efficiently B. Learn representations from data C. Replace traditional programming languages D. Perform symbolic reasoning Correct answer: B. 2. Which component of a neural network applies a non-linear transformation? A. Optimizer B. Loss function C. Activation function D. Dataset Correct answer: C. 3. What does a loss function measure? A. Model complexity B. Training speed C. Prediction error D. Number of parameters Correct answer: C. 4. Which algorithm is used to update neural network weights? A. Forward propagation B. Gradient descent C. Feature scaling D. Data normalization Correct answer: B. 5. What is backpropagation used for? A. Initializing weights B. Computing gradients C. Shuffling data D. Reducing overfitting Correct answer: B. 6. Which library is the high-level API emphasized in the book? A. PyTorch B. Scikit-learn C. Keras D. NumPy Correct answer: C. 7. What type of layer is commonly used for image data? A. Dense B. Recurrent C. Convolutional D. Embedding Correct answer: C. 8. What problem does overfitting describe? A. Model fails to learn patterns B. Model learns noise instead of signal C. Model trains too slowly D. Model has too few parameters Correct answer: B. 9. Which technique helps reduce overfitting? A. Increasing epochs B. Dropout C. Larger batch size D. Higher learning rate Correct answer: B. 10. What is the purpose of a validation set? A. Update model weights B. Test final performance only C. Tune hyperparameters D. Store unused data Correct answer: C. 11. Which activation function is commonly used in binary classification output layers? A. ReLU B. Tanh C. Softmax D. Sigmoid Correct answer: D. 12. What does an epoch represent? A. One layer in a network B. One forward pass C. One full pass over the training data D. One batch update Correct answer: C. 13. Which optimizer adapts learning rates during training? A. SGD B. RMSprop C. Perceptron D. K-means Correct answer: B. 14. What type of network is designed for sequence data? A. CNN B. DNN C. RNN D. GAN Correct answer: C. 15. What framework does Keras commonly run on top of? A. Theano B. Caffe C. TensorFlow D. MXNet Correct answer: C.

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📌 The Machine Learning “Advent Calendar” Day 15: SVM in Excel 🗂 Category: MACHINE LEARNING 🕒 Date: 2025-12-15 | ⏱️ Read time: 12 min read Instead of starting with margins and geometry, this article builds the Support Vector Machine step… #DataScience #AI #Python

Tip: Optimize PyTorch Model Performance with torch.compile Explanation: torch.compile (introduced in PyTorch 2.0) is a powerful JIT (Just-In-Time) compiler that automatically transforms your PyTorch model into highly optimized, high-performance code. It works by analyzing your model's computation graph, fusing operations, eliminating redundant computations, and compiling them into efficient kernels (e.g., using Triton for GPU acceleration). This significantly reduces Python overhead and improves memory locality, leading to substantial speedups (often 30-50% or more) during training and inference, especially on GPUs and for larger models, without requiring changes to your model architecture or training loop. The primary dynamic mode intelligently compiles subgraphs as they are encountered, providing a balance of performance and flexibility. Example:

import torch
import torch.nn as nn
import time

# Define a simple neural network
class SimpleNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.fc1 = nn.Linear(1024, 2048)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(2048, 1024)
        self.dropout = nn.Dropout(0.2)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.dropout(x)
        x = self.fc2(x)
        return x

# Prepare model and dummy data
device = "cuda" if torch.cuda.is_available() else "cpu"
model = SimpleNet().to(device)
dummy_input = torch.randn(128, 1024).to(device)
dummy_target = torch.randn(128, 1024).to(device)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.MSELoss()
num_iterations = 50

# --- Benchmark without torch.compile ---
print(f"--- Running without torch.compile on {device} ---")
start_time = time.time()
for _ in range(num_iterations):
    optimizer.zero_grad()
    output = model(dummy_input)
    loss = criterion(output, dummy_target)
    loss.backward()
    optimizer.step()
if device == "cuda":
    torch.cuda.synchronize() # Wait for GPU ops to complete
time_uncompiled = time.time() - start_time
print(f"Time without compile: {time_uncompiled:.4f} seconds\n")

# --- Benchmark with torch.compile ---
# Apply torch.compile to the model. This happens once upfront.
# The default backend 'inductor' is typically the best performing.
compiled_model = torch.compile(model)
# Ensure optimizer is correctly set up for the compiled model's parameters
# (in this case, `compiled_model` shares parameters with `model`, so no re-init needed if parameters are the same object)

print(f"--- Running with torch.compile on {device} ---")
start_time = time.time()
for _ in range(num_iterations):
    optimizer.zero_grad()
    output = compiled_model(dummy_input) # Use the compiled model
    loss = criterion(output, dummy_target)
    loss.backward()
    optimizer.step()
if device == "cuda":
    torch.cuda.synchronize() # Wait for GPU ops to complete
time_compiled = time.time() - start_time
print(f"Time with compile: {time_compiled:.4f} seconds")

if time_uncompiled > 0:
    print(f"\nSpeedup: {time_uncompiled / time_compiled:.2f}x")

━━━━━━━━━━━━━━━ By: @DataScienceM ✨

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