Comprehensive Guide to Differential Calculus for Deep Learning
Published:Jan 11, 2026 01:57
•1 min read
•Qiita DL
Analysis
This article provides a valuable reference for practitioners by summarizing the core differential calculus concepts relevant to deep learning, including vector and tensor derivatives. While concise, the usefulness would be amplified by examples and practical applications, bridging theory to implementation for a wider audience.
Key Takeaways
- •The article focuses on differentiating scalars, vectors, matrices, and tensors (nth order).
- •It covers the definitions of differential operations and organizes them based on dimensions.
- •The scope includes rules for other mathematical operations (addition, multiplication, division).
Reference
“I wanted to review the definitions of specific operations, so I summarized them.”