Why I'm Going Back to Basics
Video
As an engineer in the rapidly evolving field of AI, I don't just want to leverage GenAI APIs and build agents.
The Catalyst: DeepSeek's Innovation
Recently, the AI community has been abuzz with the groundbreaking innovations from DeepSeek.
This Chinese startup has developed a new AI model, R1, which matches or even surpasses the performance of leading models like ChatGPT, but at a fraction of the cost.
DeepSeek's approach has highlighted the importance of efficiency and deep understanding of AI fundamentals, particularly in mathematics.
This has inspired me to reset my learning and focus on building a strong mathematical foundation.
I believe having a solid math foundation will serve me well for a long and successful career.
So here’s what I am doing about it.
Quarterly Goals and Tracking
For the past year, I've been setting quarterly goals and tracking them using a tracker board that I have right above my monitor.
This board helps me visualize my progress and stay consistent.
Over the last six months, I've been able to post weekly videos and newsletters by setting clear goals. My previous quarterly goals included creating 12 videos, writing 12 news articles, losing 5 kilos, and working on specific content.
The 12-Week Plan
For this quarter, I've divided my 12-week plan into three tracks:
Work Track: This track takes up 80-90% of my time, roughly 40-50 hours per week. It includes my regular job responsibilities and tasks. The learning in these hours is immense as well, as I read code, documentation of the systems and tools I use, and of course the internet/Google to execute my day-to-day tasks.
Latest Trends Track: I dedicate 4-5 hours per week to stay updated with the latest trends in ML, DL, and GenAI. This involves reading new research papers, exploring new frameworks, and learning about new techniques. I spread this time throughout the week or focus on it during weekends.
Learning Track: This quarter, I'm focusing on resetting my learning by diving deep into the mathematics behind AI. After spending two years in this field, I've realized the importance of understanding the mathematical foundations of algorithms. This knowledge will help me optimize, fine-tune, and (hopefully) conduct deep research in ML and AI.
Learning Mathematics for AI: 12-Week Plan
To build a strong foundation in Math for AI, I've structured my learning track into a simple 12-week plan. Here's the simple breakdown:
Three core topics - Linear Algebra, Calculus, and Probability & Statistics are spread over 12 weeks - with 4 weeks/40 hours dedicated to each.
Will this make me an expert in Math?
Definitely not!
Will I be better at Math than I am today?
ABSOLUTELY!
I will share my learnings here every week. Subscribe to the newsletter, if you haven’t already, to tag along with me.
Conclusion
This 12-week plan is designed to help me balance my work, stay updated with the latest trends, and deepen my understanding of AI.
Thank you for joining me on this journey. Stay tuned for updates on my progress and feel free to share your own learning plans in the comments!
If you have any questions or need further assistance, feel free to ask!