Comprehensive Guide to Differentiation of Scalars, Vectors, Matrices, and Tensors in Deep Learning
Published:Jan 10, 2026 15:55
•1 min read
•Qiita DL
Analysis
This article provides a useful compilation of differentiation rules essential for deep learning practitioners, particularly regarding tensors. Its value lies in consolidating these rules, but its impact depends on the depth of explanation and practical application examples it provides. Further evaluation necessitates scrutinizing the mathematical rigor and accessibility of the presented derivations.
Key Takeaways
- •Covers differentiation operations for scalars, vectors, matrices, and tensors.
- •Aims to provide a consolidated reference for common differentiation rules in deep learning.
- •Includes definitions and rules for addition, multiplication, and division operations alongside differentiation.
Reference
“はじめに ディープラーニングの実装をしているとベクトル微分とかを頻繁に目にしますが、具体的な演算の定義を改めて確認したいなと思い、まとめてみました。”