Last month, I signed up for Math Academy and started solving problems on LeetCode. For the next 12 months (and beyond), I'm diving into math from scratch.
Why Math?
Math is everywhere. While you don't need to be a math genius to program well, a solid math foundation gives you a real edge. I'm relearning math to sharpen my logical reasoning, problem-solving, pattern recognition, and critical thinking skills.
With machine learning on my radar, a strong math background isn't just a bonus—it's a prerequisite. The fields of AI and ML are fundamentally built on mathematical concepts:
- Linear Algebra for understanding neural networks and data transformations
- Calculus for optimization and gradient descent
- Statistics and Probability for data analysis and model evaluation
- Graph Theory for network analysis and algorithms
The Competitive Edge
I need a competitive edge in the marketplace. Math forces you to break problems into structured, manageable parts, making you a more effective engineer. When I was younger, I studied math just to pass exams. Now, I'm rediscovering its power to deepen my understanding of the world.
My Learning Approach
- Fundamentals First: Starting with pre-calculus to ensure a solid foundation
- Daily Practice: Dedicating 1-2 hours each day to problem-solving
- Real-world Application: Connecting mathematical concepts to actual engineering problems
- Community Learning: Engaging with online math communities and study groups
Resources I'm Using
- Math Academy for structured math learning
- LeetCode for problem-solving practice
- Academind for data structures and algorithms
Over the coming months, I'll share my progress, challenges, and insights. Whether you're leveling up in software engineering, diving into AI, or simply love intellectual challenges, I believe this journey is worth the effort.