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The question asks which statement is true for the Stochastic Gradient Descent (SGD) method. Let's analyze each option:

**(a) SGD is not suitable for large datasets.**  
- **False.** SGD is particularly useful for large datasets because it updates parameters using small batches or even individual data points, which is computationally efficient and helps in handling large datasets.

**(b) Small batch sample points cannot be used for SGD convergence.**  
- **False.** Small batch sample points (mini-batch) are commonly used in SGD and can indeed help the model converge. They strike a balance between the speed of stochastic updates and the stability of batch updates.

**(c) It is not suitable for online learning where the model updates when a new data comes.**  
- **False.** SGD is actually well-suited for online learning because it updates the model parameters incrementally as each new data point becomes available.

**(d) Small batch sample points can be used for SGD convergence faster.**  
- **True.** Using small batch sample points can make convergence faster because it allows for more frequent updates, which helps the model to learn more efficiently and avoid getting stuck in local minima.

**Correct Answer: (d) Small batch sample points can be used for SGD convergence faster.**

Would you like more details or have any questions about this explanation?

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Here are five related questions for further exploration:

1. What are the main differences between Stochastic Gradient Descent (SGD) and Batch Gradient Descent?
2. How does the learning rate affect the convergence of SGD?
3. What are some common strategies to avoid overfitting when using SGD?
4. Can SGD be used for non-convex optimization problems? Why or why not?
5. How does the momentum term enhance the performance of SGD?

**Tip:** In practice, combining SGD with techniques like momentum, learning rate schedules, or adaptive methods (like Adam) can significantly improve performance and convergence speed.

Explore the concept of Stochastic Gradient Descent (SGD) and its applications in machine learning. This page provides insights into how SGD handles large datasets efficiently and its suitability for online learning scenarios. Learn about the benefits of using small batch sample points for faster convergence in SGD.

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