You're striving for algorithm perfection. How can you keep the speed and accuracy in harmony?

As a CSE student, one need to have a perfection in algorithms. For my part, I started with C++ as a language to excel in algorithms and then learned about Big O Notation which helps you to come up with an optimized approach to solve a problem.

Early in my career, I faced a slow data processing pipeline. Despite various attempts, nothing improved its speed. Realizing I had overlooked algorithm efficiency, I revisited Big O notation and discovered a function with O(n^2) complexity. Optimizing it to O(n log n) halved the processing time, underscoring the importance of understanding fundamental concepts for real-world problem-solving.

To come up with an optimized approach one needs to know about the complexities of algorithms. For example: Selection Sort takes O(n^2) time to sort data while, Merge Sort uses O(nlogn) to sort same set of data. Moving on, using a hash table to search an element is more optimized approach.

In a recent project, our model's latency was high due to inefficient data preprocessing and slow list-based searches. I optimized it by using vectorized operations with NumPy and replacing lists with hash tables. This simple change reduced latency and improved performance significantly, showing how small tweaks can lead to major improvements.

During a project, I faced performance issues with a machine learning model. I implemented rigorous testing, including stress tests with large datasets and edge cases. By adopting a test-driven development (TDD) approach, I identified and fixed bottlenecks early, ultimately enhancing both the speed and accuracy of the algorithm. This experience underscored the importance of thorough testing for robust algorithm performance.

Regular refactoring is vital for maintaining algorithm performance. In one project, our codebase became cluttered with inefficient functions over time. By systematically refactoring, we removed redundancies and streamlined the logic, resulting in a 30% increase in processing speed and improved accuracy. This experience highlighted that regular code reviews and cleanups are essential for optimal performance.

In a recent project, our machine learning model's latency was a bottleneck. By switching from nested loops to NumPy's vectorized operations and replacing lists with hash tables for lookups, we significantly reduced processing time and improved performance. This experience highlighted the importance of choosing the right data structures for efficient algorithm design.

To maintain good hands with algorithms you need to update yourself such that no one replaces you continuously. To achieve this, I'll recommend you to practice problem on Geeks for Geeks or Leetcode to excel in Data Structures and Alogrithms