'neural networks' 검색 분석 결과

Graphical representation of Neural Networks (Notes) ⤵️

Want to know why neural networks will never replace real programmers? The answer is in the picture below: because of optimization. AI has no imagination, and it doesn't seek elegant solutions. It just rushes ahead and cuts corners where it shouldn't. The problem is that today even real programmers don't know how to optimize the code (or just don't want to): you need no optimization if you can buy more powerful hardware.

This book is brilliant! It's a fun way to learn and explore maths with Python. This is exactly the way I learnt ML and neural networks back in my PhD days.

I've been coding neural networks for 2 years. Ask me anything about getting started with AI/ML! 🧠

DeepSeek just published new research that proposes changes to how neural networks are structured for breakthroughs in model cost and stability, a potential preview of efficiency gains heading into its next major release. The paper introduces mHC, a technique that stabilizes and improves AI training at a large scale while adding minimal extra computing cost. CEO Liang co-authored and personally uploaded the paper to arXiv, signaling continued hands-on involvement in the startup’s research.

Reading builds neural networks. It physically changes and grows your brain.

This is rather old, but what I love is that this is a pretty apt visualization of actual nodes in neural networks.