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research#differentiation📝 BlogAnalyzed: Jan 10, 2026 16:00

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.
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。”

PermalinkQiita DL
Research Paper#Computer Graphics, Rendering, Physically Based Rendering (PBR)🔬 ResearchAnalyzed: Jan 3, 2026 18:29

OpenPBR: Detailed Implementation and Features

Published:Dec 29, 2025 18:53
1 min read
ArXiv

Analysis

This paper provides valuable implementation details and theoretical foundations for OpenPBR, a standardized physically based rendering (PBR) shader. It's crucial for developers and artists seeking interoperability in material authoring and rendering across various visual effects (VFX), animation, and design visualization workflows. The focus on physical accuracy and standardization is a key contribution.
Reference

“The paper offers 'deeper insight into the model's development and more detailed implementation guidance, including code examples and mathematical derivations.'”

PermalinkArXiv
Education#Machine Learning Fundamentals👥 CommunityAnalyzed: Jan 3, 2026 06:28

Pen and Paper Exercises in Machine Learning (2022)

Published:Mar 21, 2025 20:07
1 min read
Hacker News

Analysis

The article's title suggests a focus on fundamental machine learning concepts and problem-solving through manual calculations and derivations. This approach can be valuable for building a deeper understanding of the underlying principles, as opposed to solely relying on software libraries. The year (2022) indicates the article is relatively recent.
Reference

“”

PermalinkHacker News
Research#Computer Vision📝 BlogAnalyzed: Jan 3, 2026 06:57

Computing Receptive Fields of Convolutional Neural Networks

Published:Nov 4, 2019 20:00
1 min read
Distill

Analysis

The article focuses on a technical aspect of convolutional neural networks (CNNs), specifically analyzing their receptive fields. This suggests a focus on understanding and potentially optimizing the internal workings of CNNs. The source, Distill, is known for its high-quality, in-depth explanations, indicating a likely rigorous and detailed treatment of the subject.
Reference

“Detailed derivations and open-source code to analyze the receptive fields of convnets.”

PermalinkDistill