Understanding Importance Derivation
The article discusses the concept of importance derivation in neural networks. It explains how differentiation is applied in practical scenarios, such as calculating velocity and acceleration. The author also introduces an AI code reviewer project called git-lrc, inviting feedback and contributions from the community.
- ▪The article is part of a series on understanding neural networks.
- ▪It highlights the practical application of differentiation in physics and mechanical engineering.
- ▪The author is developing an AI code reviewer named git-lrc, which is free and open-source.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 1403545) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Ganesh Kumar Posted on May 16 Understanding Importance Derivation #ai #learning #science #showdev Understanding Neural Network (5 Part Series) 1 Introduction to Neural Networks 2 Internal Architecture of Neural Networks 3 Action Potentials in Neurons 4 How Calculations Happen in a Neural Network 5 Understanding Importance Derivation Hello, I'm Ganesh. I'm building git-lrc, an AI code reviewer that runs on every commit. It is free, unlimited, and source-available on GitHub.
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