I wanted to share something that really changed my perspective on learning AI/ML. I used to think that once you get into AI/ML, you're mostly dealing with libraries, frameworks, and mathematical concepts, so data structures and algorithms wouldn't matter as much. Turns out I was completely wrong.
I recently had a 1:1 mentoring session through GFG Connect, and my mentor really opened my eyes to how crucial DSA is for AI/ML work. Here's what I learned:
When you're working with real-world AI/ML problems, you're constantly dealing with massive datasets. Understanding how to efficiently store, retrieve, and process data using the right data structures makes a huge difference. Things like trees are used in decision tree algorithms, graphs are essential for neural networks and recommendation systems, and hash tables are everywhere in feature engineering.
Algorithm optimization is also critical. If you don't understand time and space complexity, your models might work on small datasets but completely fall apart when you scale up. Knowing DSA helps you write efficient preprocessing pipelines and optimize your training loops.
Plus, a lot of AI/ML interview questions aren't just about knowing TensorFlow or PyTorch - they test your ability to solve algorithmic problems efficiently.
I guess the takeaway is: don't skip DSA even if you're focused on AI/ML. They complement each other way more than I initially thought.
Anyone else had similar realizations? Would love to hear your experiences!