Hi everyone,
I am looking for a study partner to dive deep into the mathematical foundations of Deep Learning. I have a solid grasp of the core concepts (architectures, backpropagation, etc.), but I want to bridge the gap by mastering the rigorous math behind them (Matrix Calculus, Probability Theory, Optimization, etc.).
Who I’m looking for:
- Someone who already understands most Deep Learning concepts and has at least a foundational level of the associated math.
- A serious learner who wants to go through textbooks (like Goodfellow’s Deep Learning or Mathematics for Machine Learning) or research papers.
My Goal:
I want to discuss and "stress-test" my understanding by speaking through complex problems. I’m happy to exchange ideas and while I’m looking to solidify the math, I can contribute by "brainstorming unique solutions for paper ideas" or "PyTorch implementation".
Format:
- Weekly or bi-weekly syncs (Discord/Zoom) to discuss specific chapters or concepts.
- Solving/deriving formulas together.
If you’re interested in a serious, high-level collaboration to master the "why" behind the "how," please drop a comment or DM me!
[link] [comments]
