Day206 - Leetcode: Python 217 & SQL 175,176 & DL Review

📚 The Start of Recording TIL Summer ‘24 : Why Did I Start This Project? 🚀

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✨ A New Chapter in TIL '25: Reflecting, Growing, and Moving Forward 📖

Python 217: Contains Duplicate / SQL 175,176: Second-highest Salary / DL Review: Transfer Learning & Fine-Tuning & CNNs

🟩 Python Review

217. Contains Duplicate

• Given an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct.

### My answer
class Solution(object):
    def containsDuplicate(self, nums: List[int] -> Bool):
        if len(nums) != sort(len(nums)) # A bit confused
        else true
        return false

        """
        :type nums: List[int]
        :rtype: bool

        ---> Check numbers of list and compare 
        """
        
### Solution 
class Solution(object):
    def containsDuplicate(self, nums: List[int]) -> bool:
        if len(nums) == len(set(nums)): # Use SET
            return False
        return True

From my attempt,

• Many syntax errors. I was confused about how to compare the number of list indexes.
• Should be -> bool (lowercase), not -> Bool.
• I missed a colon (:) after the if statement.

From the corrected solution,

• set(nums) removes duplicates because a set cannot contain repeated values.
• The logic:
  • If the lengths are equal -> no duplicates; return true.
  • If the lengths are unequal -> duplicates exist; return false,

🟨 SQL Review

175. Combine Two Tables

-- My Answer 
select a.firstName, a.lastName, b.city, b.state 
from Person a 
	outer join Address b on a.personId = b.personId;

-- Solution
SELECT a.firstName, a.lastName, b.city, b.state
FROM Person a
LEFT JOIN Address b 
    ON a.personId = b.personId;

In PostgreSQL, the OUTER JOIN must be specified more explicitly — we can use LEFT JOIN, RIGHT JOIN, or FULL OUTER JOIN depending on the requirement.

176. Second-Highest Salary

-- My Answer
select dist(salary) from Employee
Limit 1;
-- Do I have to use group by? ==> NO, You do not need to. 
-- Do I have to put any constraints for null value? ==> No, Postgre automatically ignores Nulls.

-- Solution
select distinct salary from Employee
order by salary desc
offset 1 limit 1;

From my attempt.

• DIST () is not a valid function in PostgreSQL.
  • I must write up SELECT DISTINCT.
• I forgot to use offset to calculate the second-highest one.

From the solution,

• offset skips a certain number of rows. From here, we skip the first highest salary, and then provide the second highest one as requested.

🟦 DL Review

1. Transfer Learning & Fine-Tuning

Transfer learning leverages a model pretrained on a large dataset (e.g., ImageNet, BERT), and adapts it to a new task with limited data.

• Feature extraction: Freeze pre-trained layers and use them as feature generators.
• Fine-tuning: Unfreeze some layers and retrain with task-specific data.

Why It Matters

• Reduces training time and data requirements.
• Often achieves **state-of-the-art results **on small datasets.
• Common in NLP, CV, and speech applications.

“Transfer learning uses pre-trained models as a starting point and adapts them to new tasks through fine-tuning. This approach saves computation, requires less labeled data, and usually improves generalization.”

MLOps Angle

• When deploying, choosing how much to fine-tune matters for latency and inference costs. Pretrained embeddings/models also introduce external dependencies, so versioning and monitoring are key.

2. Convolutional Neural Networks (CNNs)

CNNs are specialized neural networks designed for grid-like data (e.g., images).

• Convolutions: Learn spatial filters that detect local features.
• Pooling: Reduces dimensionality, adds translation invariance.
• Stacking layers: Builds hierarchical representations (edges → shapes → objects).

Why It Matters

• Standard for image classification, object detection, and segmentation.
• Efficient due to parameter sharing and local connectivity.
• Inspiration behind modern architecture (e.g., ResNets, EfficientNet, ConvNeXt),

“Convolutional Neural Networks use parameter-sharing filters to detect local patterns, making them efficient and powerful for image tasks. By stacking layers, CNNs learn hierarchical representations, from edges to complex objects.”

MLOps Angle

• CNNs dominate computer vision pipelines. In deployment, they must be optimized for inference (via quantization, pruning, and ONNX export) to meet latency constraints on edge devices.