ML interview Question 📚

What is Quantization in machine learning?

Quantization the process of reducing the precision of the numbers used to represent a model's parameters, such as weights and activations. This is often done by converting 32-bit floating-point numbers (commonly used in training) to lower precision formats, like 16-bit or 8-bit integers.

Quantization is primarily used during model inference to:
1. Reduce model size: Lower precision numbers require less memory.
2. Improve computational efficiency: Operations on lower-precision data types are faster and require less power.
3. Speed up inference: Smaller models can be loaded faster, improving performance on edge devices like smartphones or IoT devices.

Quantization can lead to a small loss in model accuracy, as reducing precision can introduce rounding errors. But in many cases, the trade-off between accuracy and efficiency is worthwhile, especially for deployment on resource-constrained devices.

There are different types of quantization:
1. Post-training quantization: Applied after the model has been trained.
2.Quantization-aware training (QAT): Takes quantization into account during the training process to minimize the accuracy drop.

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🛠️ Must-Know SQL Commands & Functions ✅

1. SELECT – Retrieve data 
   › SELECT * FROM customers;

2. WHERE – Filter rows 
   › SELECT * FROM orders WHERE amount > 500;

3. ORDER BY – Sort results 
   › SELECT name FROM users ORDER BY age DESC;

4. GROUP BY – Aggregate data 
   › SELECT department, COUNT(*) FROM employees GROUP BY department;

5. JOIN – Combine tables 
   › SELECT a.name, b.salary FROM employees a JOIN salaries b ON a.id = b.emp_id;

6. INSERT INTO – Add new data 
   › INSERT INTO users (name, age) VALUES ('John', 30);

7. UPDATE – Modify existing data 
   › UPDATE products SET price = 100 WHERE id = 1;

8. DELETE – Remove data 
   › DELETE FROM logs WHERE date < '2023-01-01';

9. LIKE – Pattern matching 
   › SELECT * FROM customers WHERE name LIKE 'A%';

10. LIMIT – Restrict result rows 
    › SELECT * FROM sales LIMIT 10;

💡 Tip: Practice on real datasets. Learn JOIN and GROUP BY early—they’re game changers!

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Placement Resources:
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