Search:
Match:
35 results
research#calculus📝 BlogAnalyzed: Jan 11, 2026 02:00

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.
Reference

I wanted to review the definitions of specific operations, so I summarized them.

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。

research#llm📝 BlogAnalyzed: Jan 4, 2026 14:43

ChatGPT Explains Goppa Code Decoding with Calculus

Published:Jan 4, 2026 13:49
1 min read
Qiita ChatGPT

Analysis

This article highlights the potential of LLMs like ChatGPT to explain complex mathematical concepts, but also raises concerns about the accuracy and depth of the explanations. The reliance on ChatGPT as a primary source necessitates careful verification of the information presented, especially in technical domains like coding theory. The value lies in accessibility, not necessarily authority.

Key Takeaways

Reference

なるほど、これは パターソン復号法における「エラー値の計算」で微分が現れる理由 を、関数論・有限体上の留数 の観点から説明するという話ですね。

Education#AI/ML Math Resources📝 BlogAnalyzed: Jan 3, 2026 06:58

Seeking AI/ML Math Resources

Published:Jan 2, 2026 16:50
1 min read
r/learnmachinelearning

Analysis

This is a request for recommendations on math resources relevant to AI/ML. The user is a self-studying student with a Python background, seeking to strengthen their mathematical foundations in statistics/probability and calculus. They are already using Gilbert Strang's linear algebra lectures and dislike Deeplearning AI's teaching style. The post highlights a common need for focused math learning in the AI/ML field and the importance of finding suitable learning materials.
Reference

I'm looking for resources to study the following: -statistics and probability -calculus (for applications like optimization, gradients, and understanding models) ... I don't want to study the entire math courses, just what is necessary for AI/ML.

Analysis

This paper establishes a direct link between entropy production (EP) and mutual information within the framework of overdamped Langevin dynamics. This is significant because it bridges information theory and nonequilibrium thermodynamics, potentially enabling data-driven approaches to understand and model complex systems. The derivation of an exact identity and the subsequent decomposition of EP into self and interaction components are key contributions. The application to red-blood-cell flickering demonstrates the practical utility of the approach, highlighting its ability to uncover active signatures that might be missed by conventional methods. The paper's focus on a thermodynamic calculus based on information theory suggests a novel perspective on analyzing and understanding complex systems.
Reference

The paper derives an exact identity for overdamped Langevin dynamics that equates the total EP rate to the mutual-information rate.

Analysis

This paper introduces a novel 4D spatiotemporal formulation for solving time-dependent convection-diffusion problems. By treating time as a spatial dimension, the authors reformulate the problem, leveraging exterior calculus and the Hodge-Laplacian operator. The approach aims to preserve physical structures and constraints, leading to a more robust and potentially accurate solution method. The use of a 4D framework and the incorporation of physical principles are the key strengths.
Reference

The resulting formulation is based on a 4D Hodge-Laplacian operator with a spatiotemporal diffusion tensor and convection field, augmented by a small temporal perturbation to ensure nondegeneracy.

Paper#LLM🔬 ResearchAnalyzed: Jan 3, 2026 09:24

LLMs Struggle on Underrepresented Math Problems, Especially Geometry

Published:Dec 30, 2025 23:05
1 min read
ArXiv

Analysis

This paper addresses a crucial gap in LLM evaluation by focusing on underrepresented mathematics competition problems. It moves beyond standard benchmarks to assess LLMs' reasoning abilities in Calculus, Analytic Geometry, and Discrete Mathematics, with a specific focus on identifying error patterns. The findings highlight the limitations of current LLMs, particularly in Geometry, and provide valuable insights into their reasoning processes, which can inform future research and development.
Reference

DeepSeek-V3 has the best performance in all three categories... All three LLMs exhibited notably weak performance in Geometry.

Analysis

This article likely presents advanced mathematical research. The title suggests a focus on differential geometry and algebraic structures. The terms 'torsion-free bimodule connections' and 'maximal prolongation' indicate a technical and specialized subject matter. The source, ArXiv, confirms this is a pre-print server for scientific papers.
Reference

Analysis

This paper introduces a novel machine learning framework, Schrödinger AI, inspired by quantum mechanics. It proposes a unified approach to classification, reasoning, and generalization by leveraging spectral decomposition, dynamic evolution of semantic wavefunctions, and operator calculus. The core idea is to model learning as navigating a semantic energy landscape, offering potential advantages over traditional methods in terms of interpretability, robustness, and generalization capabilities. The paper's significance lies in its physics-driven approach, which could lead to new paradigms in machine learning.
Reference

Schrödinger AI demonstrates: (a) emergent semantic manifolds that reflect human-conceived class relations without explicit supervision; (b) dynamic reasoning that adapts to changing environments, including maze navigation with real-time potential-field perturbations; and (c) exact operator generalization on modular arithmetic tasks, where the system learns group actions and composes them across sequences far beyond training length.

Analysis

This paper presents a mathematical analysis of the volume and surface area of the intersection of two cylinders. It generalizes the concept of the Steinmetz solid, a well-known geometric shape formed by the intersection of two or three cylinders. The paper likely employs integral calculus and geometric principles to derive formulas for these properties. The focus is on providing a comprehensive mathematical treatment rather than practical applications.
Reference

The paper likely provides a detailed mathematical treatment of the intersection of cylinders.

Career#AI Engineering📝 BlogAnalyzed: Dec 27, 2025 12:02

How I Cracked an AI Engineer Role

Published:Dec 27, 2025 11:04
1 min read
r/learnmachinelearning

Analysis

This article, sourced from Reddit's r/learnmachinelearning, offers practical advice for aspiring AI engineers based on the author's personal experience. It highlights the importance of strong Python skills, familiarity with core libraries like NumPy, Pandas, Scikit-learn, PyTorch, and TensorFlow, and a solid understanding of mathematical concepts. The author emphasizes the need to go beyond theoretical knowledge and practice implementing machine learning algorithms from scratch. The advice is tailored to the competitive job market of 2025/2026, making it relevant for current job seekers. The article's strength lies in its actionable tips and real-world perspective, providing valuable guidance for those navigating the AI job market.
Reference

Python is a must. Around 70–80% of AI ML job postings expect solid Python skills, so there is no way around it.

Analysis

This paper challenges the common interpretation of the conformable derivative as a fractional derivative. It argues that the conformable derivative is essentially a classical derivative under a time reparametrization, and that claims of novel fractional contributions using this operator can be understood within a classical framework. The paper's importance lies in clarifying the mathematical nature of the conformable derivative and its relationship to fractional calculus, potentially preventing misinterpretations and promoting a more accurate understanding of memory-dependent phenomena.
Reference

The conformable derivative is not a fractional operator but a useful computational tool for systems with power-law time scaling, equivalent to classical differentiation under a nonlinear time reparametrization.

Research#Mathematics🔬 ResearchAnalyzed: Jan 10, 2026 07:14

Applying a Noncompactness Measure to Fractional Hilfer Equations

Published:Dec 26, 2025 11:58
1 min read
ArXiv

Analysis

This research explores a specific mathematical domain, focusing on fractional calculus and integral equations. The application of a measure of noncompactness suggests an investigation into the existence and properties of solutions within a given mathematical framework.
Reference

An application to a system of $(k,ρ)$-fractional Hilfer integral equations via a measure of noncompactness.

Research#Calculus🔬 ResearchAnalyzed: Jan 10, 2026 07:14

Deep Dive into the Tensor-Plus Calculus: A New Mathematical Framework

Published:Dec 26, 2025 10:26
1 min read
ArXiv

Analysis

Without the actual article content, a substantive critique is impossible. We lack the necessary information to analyze the paper's contributions or implications, though the title suggests a potentially innovative approach.
Reference

Based on the prompt, there is no subordinate information to quote from.

Research#Calculus🔬 ResearchAnalyzed: Jan 10, 2026 07:35

Advanced Fractional Calculus: New Results and Applications

Published:Dec 24, 2025 16:44
1 min read
ArXiv

Analysis

This ArXiv paper delves into the complex world of fractional calculus, specifically focusing on the Prabhakar type fractional derivative. The research likely presents novel mathematical results and explores their potential applications.
Reference

The paper investigates the nth-Level Prabhakar Type Fractional Derivative.

Research#Calculus🔬 ResearchAnalyzed: Jan 10, 2026 07:35

Analysis of Prabhakar Fractional Derivative in Boundary Value Problems

Published:Dec 24, 2025 16:07
1 min read
ArXiv

Analysis

This article, sourced from ArXiv, focuses on a specific mathematical concept: the Prabhakar fractional derivative. It likely presents new mathematical solutions or expands on existing methods for solving boundary value problems within this framework.
Reference

The context refers to a boundary value problem involving the Prabhakar fractional derivative.

Research#Tensor Calculus🔬 ResearchAnalyzed: Jan 10, 2026 08:56

TensoriaCalc: Simplifying Tensor Calculus in Wolfram Language

Published:Dec 21, 2025 16:27
1 min read
ArXiv

Analysis

This ArXiv article highlights the release of TensoriaCalc, a package designed to make tensor calculus more accessible within the Wolfram Language ecosystem. The paper's user-friendly approach could benefit researchers and students working with tensor mathematics.
Reference

TensoriaCalc is a user-friendly tensor calculus package for the Wolfram Language.

Research#Actuators🔬 ResearchAnalyzed: Jan 10, 2026 09:16

Fractional-Order Modeling and Optimization for Soft Actuators

Published:Dec 20, 2025 04:46
1 min read
ArXiv

Analysis

This research explores a novel modeling approach for soft actuators, potentially leading to improved control and performance. The use of fractional-order calculus and particle swarm optimization suggests a sophisticated approach to addressing the inherent nonlinearities in these systems.
Reference

The study focuses on fractional-order modeling for nonlinear soft actuators via Particle Swarm Optimization.

Analysis

This article presents a novel approach using a physics-informed neural network (PINN) incorporating fractional differential equations for battery state estimation. The use of fractional calculus suggests an attempt to model complex battery behavior more accurately than traditional methods. The application to battery state estimation is a practical and relevant area. The source, ArXiv, indicates this is a pre-print or research paper, suggesting the work is likely cutting-edge but not yet peer-reviewed.
Reference

Research#llm📝 BlogAnalyzed: Dec 28, 2025 21:57

Pedro Domingos: Tensor Logic Unifies AI Paradigms

Published:Dec 8, 2025 00:36
1 min read
ML Street Talk Pod

Analysis

The article discusses Pedro Domingos's Tensor Logic, a new programming language designed to unify the disparate approaches to artificial intelligence. Domingos argues that current AI is divided between deep learning, which excels at learning from data but struggles with reasoning, and symbolic AI, which excels at reasoning but struggles with data. Tensor Logic aims to bridge this gap by allowing for both logical rules and learning within a single framework. The article highlights the potential of Tensor Logic to enable transparent and verifiable reasoning, addressing the issue of AI 'hallucinations'. The article also includes sponsor messages.
Reference

Think of it like this: Physics found its language in calculus. Circuit design found its language in Boolean logic. Pedro argues that AI has been missing its language - until now.

Research#llm👥 CommunityAnalyzed: Jan 4, 2026 08:08

Math for Computer Science and Machine Learning

Published:Mar 22, 2025 09:42
1 min read
Hacker News

Analysis

This article, sourced from Hacker News, likely discusses the importance of mathematical foundations for computer science and machine learning. The title suggests a focus on the mathematical concepts relevant to these fields, potentially including linear algebra, calculus, probability, and statistics. The 'pdf' tag indicates the content is likely a downloadable document, possibly a textbook, lecture notes, or a curated list of resources.

Key Takeaways

    Reference

    Research#llm📝 BlogAnalyzed: Jan 3, 2026 01:47

    The Elegant Math Behind Machine Learning

    Published:Nov 4, 2024 21:02
    1 min read
    ML Street Talk Pod

    Analysis

    This article discusses the fundamental mathematical principles underlying machine learning, emphasizing its growing influence on various fields and its impact on decision-making processes. It highlights the historical roots of these mathematical concepts, tracing them back to the 17th and 18th centuries. The article underscores the importance of understanding the mathematical foundations of AI to ensure its safe and effective use, suggesting a potential link between artificial and natural intelligence. It also mentions the role of computer science and advancements in computer chips in the development of AI.
    Reference

    To make safe and effective use of artificial intelligence, we need to understand its profound capabilities and limitations, the clues to which lie in the math that makes machine learning possible.

    Research#llm👥 CommunityAnalyzed: Jan 4, 2026 08:48

    The matrix calculus you need for deep learning (2018)

    Published:Jul 30, 2023 17:18
    1 min read
    Hacker News

    Analysis

    This article likely discusses the mathematical foundations of deep learning, specifically focusing on matrix calculus. The year 2018 suggests it might be a bit dated, but the core concepts remain relevant. The source, Hacker News, indicates it's likely a technical discussion aimed at a knowledgeable audience.

    Key Takeaways

      Reference

      Research#llm👥 CommunityAnalyzed: Jan 4, 2026 08:27

      The Modern Mathematics of Deep Learning

      Published:Jun 12, 2021 16:37
      1 min read
      Hacker News

      Analysis

      This article likely discusses the mathematical foundations underpinning deep learning, such as linear algebra, calculus, probability, and optimization. It might delve into topics like backpropagation, gradient descent, and the mathematical properties of neural networks. The source, Hacker News, suggests a technical audience.

      Key Takeaways

        Reference

        Research#Deep Learning👥 CommunityAnalyzed: Jan 10, 2026 16:34

        Deep Learning: Mastering the Matrix Calculus Foundation

        Published:Apr 2, 2021 22:45
        1 min read
        Hacker News

        Analysis

        This article, though dated from 2018, likely provides a fundamental overview of matrix calculus, a crucial topic for understanding and implementing deep learning models. Reviewing such introductory material remains valuable for those new to the field, offering a solid basis for more complex concepts.
        Reference

        The article's presence on Hacker News suggests it was considered informative to a technical audience.

        Research#Deep Learning👥 CommunityAnalyzed: Jan 10, 2026 16:35

        Deep Learning: A Mathematical Engineering Perspective

        Published:Mar 8, 2021 13:23
        1 min read
        Hacker News

        Analysis

        The article's focus on the mathematical underpinnings of deep learning is crucial for understanding its capabilities and limitations. It highlights the importance of rigorous engineering practices in this rapidly evolving field.
        Reference

        The article likely discusses the mathematical principles that form the foundation of deep learning algorithms.

        Research#llm👥 CommunityAnalyzed: Jan 4, 2026 08:41

        Matrix calculus for deep learning part 2

        Published:May 30, 2020 05:35
        1 min read
        Hacker News

        Analysis

        This article likely discusses the mathematical foundations of deep learning, specifically focusing on matrix calculus. Part 2 suggests a continuation of a previous discussion, implying a series or a follow-up. The source, Hacker News, indicates a technical audience interested in programming and computer science.

        Key Takeaways

          Reference

          Research#Calculus👥 CommunityAnalyzed: Jan 10, 2026 16:45

          Deep Dive: Matrix Calculus in Deep Learning

          Published:Nov 29, 2019 02:29
          1 min read
          Hacker News

          Analysis

          The article's focus on matrix calculus highlights a foundational aspect of deep learning, crucial for understanding how neural networks learn and optimize. The Hacker News context suggests a broader audience interested in the technical underpinnings of AI.
          Reference

          The article is sourced from Hacker News.

          Research#Math👥 CommunityAnalyzed: Jan 10, 2026 16:58

          Essential Math for Machine Learning: A Hacker News Perspective

          Published:Aug 17, 2018 07:34
          1 min read
          Hacker News

          Analysis

          The article's focus on Hacker News suggests it will highlight practical math knowledge relevant for those actively involved in machine learning development. It will likely cover topics like linear algebra, calculus, and probability, offering insights for practitioners.

          Key Takeaways

          Reference

          The source is Hacker News.

          Research#Calculus👥 CommunityAnalyzed: Jan 10, 2026 17:00

          Demystifying Matrix Calculus for Deep Learning

          Published:Jun 29, 2018 06:23
          1 min read
          Hacker News

          Analysis

          This Hacker News article likely focuses on explaining the mathematical foundations of deep learning, particularly matrix calculus. A clear understanding of these concepts is crucial for anyone working in the field.
          Reference

          The article likely discusses matrix calculus.

          Research#Calculus👥 CommunityAnalyzed: Jan 10, 2026 17:04

          Deep Dive into Matrix Calculus for Deep Learning

          Published:Jan 30, 2018 17:40
          1 min read
          Hacker News

          Analysis

          This Hacker News article likely discusses the mathematical foundations of deep learning, focusing on matrix calculus. The article's quality depends heavily on its ability to explain complex concepts accessibly and offer novel insights, but without a concrete article, the impact is uncertain.
          Reference

          The article's key fact cannot be determined without the content.

          Research#machine learning👥 CommunityAnalyzed: Jan 3, 2026 15:45

          Mathematics of Machine Learning (2016)

          Published:Sep 1, 2017 07:19
          1 min read
          Hacker News

          Analysis

          The article title indicates a focus on the mathematical foundations of machine learning, likely covering topics such as linear algebra, calculus, probability, and statistics. The year 2016 suggests the content might be slightly dated but still relevant for understanding core concepts. The Hacker News source implies a technical audience.
          Reference

          Research#llm📝 BlogAnalyzed: Dec 26, 2025 16:47

          Calculus on Computational Graphs: Backpropagation

          Published:Aug 31, 2015 00:00
          1 min read
          Colah

          Analysis

          This article provides a clear and concise explanation of backpropagation, emphasizing its crucial role in making deep learning computationally feasible. It highlights the algorithm's efficiency compared to naive implementations and its broader applicability beyond deep learning, such as in weather forecasting and numerical stability analysis. The article also points out that backpropagation, or reverse-mode differentiation, has been independently discovered in various fields. The author effectively conveys the fundamental nature of backpropagation as a technique for rapid derivative calculation, making it a valuable tool in diverse numerical computing scenarios. The article's accessibility makes it suitable for readers with varying levels of technical expertise.
          Reference

          Backpropagation is the key algorithm that makes training deep models computationally tractable.