Search: NumPy - ai.jp.net

research #deep learning 📝 BlogAnalyzed: Jan 18, 2026 14:46

SmallPebble: Revolutionizing Deep Learning with a Minimalist Approach

Published:Jan 18, 2026 14:44

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1 min read

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r/MachineLearning

Analysis

SmallPebble offers a refreshing take on deep learning, providing a from-scratch library built entirely in NumPy! This minimalist approach allows for a deeper understanding of the underlying principles and potentially unlocks exciting new possibilities for customization and optimization.

Key Takeaways

•SmallPebble is a deep learning library built from scratch.
•It is implemented entirely using NumPy.
•This minimalist design promotes a deeper understanding of deep learning concepts.

Reference

“This article highlights the development of SmallPebble, a minimalist deep learning library written from scratch in NumPy.”

Permalink r/MachineLearning

research #ai 📝 BlogAnalyzed: Jan 18, 2026 10:30

Crafting AI Brilliance: Python Powers a Tic-Tac-Toe Master!

Published:Jan 18, 2026 10:17

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1 min read

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Qiita AI

Analysis

This article details a fascinating journey into building a Tic-Tac-Toe AI from scratch using Python! The use of bitwise operations for calculating legal moves is a clever and efficient approach, showcasing the power of computational thinking in game development.

Key Takeaways

•The project utilizes Python 3.13 and NumPy 2.3.5, demonstrating modern software development practices.
•The focus on bitwise operations highlights a computationally efficient method for game logic.
•This initiative provides a great educational resource for learning about AI development and game programming.

Reference

“The article's program is running on Python version 3.13 and numpy version 2.3.5.”

Permalink Qiita AI

research #llm 📝 BlogAnalyzed: Jan 13, 2026 19:30

Deep Dive into LLMs: A Programmer's Guide from NumPy to Cutting-Edge Architectures

Published:Jan 13, 2026 12:53

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1 min read

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Zenn LLM

Analysis

This guide provides a valuable resource for programmers seeking a hands-on understanding of LLM implementation. By focusing on practical code examples and Jupyter notebooks, it bridges the gap between high-level usage and the underlying technical details, empowering developers to customize and optimize LLMs effectively. The inclusion of topics like quantization and multi-modal integration showcases a forward-thinking approach to LLM development.

Key Takeaways

•Focuses on practical code implementation with Python and NumPy for LLMs.
•Covers a wide range of advanced LLM topics, including quantization, multi-modal integration, and optimization.
•Provides hands-on learning through Jupyter Notebooks with detailed annotations.

Reference

“This series dissects the inner workings of LLMs, from full scratch implementations with Python and NumPy, to cutting-edge techniques used in Qwen-32B class models.”

Permalink Zenn LLM

infrastructure #numpy 📝 BlogAnalyzed: Jan 10, 2026 04:42

NumPy Deep Learning Log 6: Mastering Multidimensional Arrays

Published:Jan 10, 2026 00:42

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1 min read

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Qiita DL

Analysis

This article, based on interaction with Gemini, provides a basic introduction to NumPy's handling of multidimensional arrays. While potentially helpful for beginners, it lacks depth and rigorous examples necessary for practical application in complex deep learning projects. The dependency on Gemini's explanations may limit the author's own insights and the potential for novel perspectives.

Key Takeaways

•Article discusses NumPy's handling of multidimensional arrays.
•Content is based on a conversation with the Gemini AI.
•The development environment is VScode + Anaconda.

Reference

“When handling multidimensional arrays of 3 or more dimensions, imagine a 'solid' in your head...”

Permalink Qiita DL

research #llm 📝 BlogAnalyzed: Jan 10, 2026 05:00

Controlling LLM Output Variation: An Empirical Look at Temperature, Top-p, Top-k, and Repetition Penalty

Published:Jan 9, 2026 16:34

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1 min read

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Zenn LLM

Analysis

This article provides a hands-on exploration of key LLM output parameters, focusing on their impact on text generation variability. By using a minimal experimental setup without relying on external APIs, it offers a practical understanding of these parameters for developers. The limitation of not assessing model quality is a reasonable constraint given the article's defined scope.

Key Takeaways

•The article demonstrates the behavioral differences of Temperature, Top-p, and Top-k sampling strategies.
•It utilizes a minimal experimental setup based on Python and NumPy.
•The focus is on understanding parameter effects, not evaluating overall model performance.

Reference

“本記事のコードは、Temperature / Top-p / Top-k の挙動差を API なしで体感する最小実験です。”

Permalink Zenn LLM

research #numpy 📝 BlogAnalyzed: Jan 10, 2026 04:42

NumPy Fundamentals: A Beginner's Deep Learning Journey

Published:Jan 9, 2026 10:35

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1 min read

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Qiita DL

Analysis

This article details a beginner's experience learning NumPy for deep learning, highlighting the importance of understanding array operations. While valuable for absolute beginners, it lacks advanced techniques and assumes a complete absence of prior Python knowledge. The dependence on Gemini suggests a need for verifying the AI-generated content for accuracy and completeness.

Key Takeaways

•Focuses on NumPy basics for deep learning.
•Emphasizes axis, broadcasting, and nditer.
•Relies on conversation with Gemini for content.

Reference

“NumPyの多次元配列操作で混乱しないための3つの鉄則：axis・ブロードキャスト・nditer”

Permalink Qiita DL

Research #machine learning 📝 BlogAnalyzed: Dec 28, 2025 21:58

SmolML: A Machine Learning Library from Scratch in Python (No NumPy, No Dependencies)

Published:Dec 28, 2025 14:44

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1 min read

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r/learnmachinelearning

Analysis

This article introduces SmolML, a machine learning library created from scratch in Python without relying on external libraries like NumPy or scikit-learn. The project's primary goal is educational, aiming to help learners understand the underlying mechanisms of popular ML frameworks. The library includes core components such as autograd engines, N-dimensional arrays, various regression models, neural networks, decision trees, SVMs, clustering algorithms, scalers, optimizers, and loss/activation functions. The creator emphasizes the simplicity and readability of the code, making it easier to follow the implementation details. While acknowledging the inefficiency of pure Python, the project prioritizes educational value and provides detailed guides and tests for comparison with established frameworks.

Key Takeaways

•SmolML is a Python-based ML library built from scratch, emphasizing educational value.
•It provides implementations of core ML components without external dependencies, promoting understanding of underlying mechanisms.
•The project offers detailed guides and tests for comparison with established ML frameworks.

Reference

“My goal was to help people learning ML understand what's actually happening under the hood of frameworks like PyTorch (though simplified).”

Permalink r/learnmachinelearning

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

How I Cracked an AI Engineer Role

Published:Dec 27, 2025 11:04

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1 min read

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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.

Key Takeaways

•Master Python and core AI/ML libraries.
•Practice implementing algorithms from scratch.
•Strengthen your understanding of linear algebra and calculus.

Reference

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

Permalink r/learnmachinelearning

Research #llm 📝 BlogAnalyzed: Dec 29, 2025 01:43

Understanding Tensor Data Structures with Go

Published:Dec 27, 2025 08:08

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1 min read

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Zenn ML

Analysis

This article from Zenn ML details the implementation of tensors, a fundamental data structure for automatic differentiation in machine learning, using the Go programming language. The author prioritizes understanding the concept by starting with a simple implementation and then iteratively improving it based on existing libraries like NumPy. The article focuses on the data structure of tensors and optimization techniques learned during the process. It also mentions a related article on automatic differentiation. The approach emphasizes a practical, hands-on understanding of tensors, starting from basic concepts and progressing to more efficient implementations.

Key Takeaways

•The article focuses on implementing tensors in Go.
•The author prioritizes understanding over initial performance.
•The implementation is improved by referencing existing libraries like NumPy.

Reference

“The article introduces the implementation of tensors, a fundamental data structure for automatic differentiation in machine learning.”

Permalink Zenn ML

Research #LLM 👥 CommunityAnalyzed: Jan 10, 2026 15:36

NumPy Implementation of Llama 3: A Novel Approach

Published:May 16, 2024 13:53

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1 min read

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Hacker News

Analysis

The implementation of Llama 3 in pure NumPy on Hacker News suggests a focus on accessibility and potential for educational purposes, highlighting a departure from optimized frameworks. This approach may open doors for easier understanding and modification of the model's inner workings.

Key Takeaways

•Implementation focuses on using pure NumPy, likely for educational or research purposes.
•This approach contrasts with optimized frameworks, prioritizing understandability over performance.
•The news originates from Hacker News, suggesting community discussion and developer interest.

Reference

“The article's context provides no direct quotes.”

Permalink Hacker News

Research #CNN 👥 CommunityAnalyzed: Jan 10, 2026 16:49

Building a CNN from Scratch with NumPy: A Deep Dive

Published:May 31, 2019 20:58

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1 min read

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Hacker News

Analysis

This article likely details the implementation of a Convolutional Neural Network (CNN) using only NumPy, a fundamental Python library for numerical computation. Such a project is valuable for educational purposes and provides a deeper understanding of CNN architecture, but its practical applications might be limited by performance constraints.

Key Takeaways

•Demonstrates the underlying math and mechanics of CNNs.
•Provides a hands-on learning experience for AI enthusiasts.
•Suitable for educational purposes and understanding CNN principles.

Reference

“The article likely explains how to build a CNN using only NumPy.”

Permalink Hacker News

Software Development #Machine Learning Application 👥 CommunityAnalyzed: Jan 3, 2026 15:49

Incorporating Machine Learning into a Shipping Company's Day Job

Published:Dec 10, 2018 19:49

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1 min read

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Hacker News

Analysis

The article describes a developer's challenge in finding a practical application for machine learning within their current role at a shipping company. The core issue is identifying a problem that necessitates ML over traditional database solutions. The developer has the technical skills (PyTorch, NumPy, Pandas) but lacks a clear use case. The supportive boss provides an opportunity for side projects.

Key Takeaways

•The primary challenge is identifying a suitable problem for ML application within a shipping company.
•The developer possesses the technical skills but lacks a concrete use case.
•The supportive work environment allows for exploration of side projects.
•The core issue is the perceived overlap in functionality between ML and traditional database solutions.

Reference

“I'd like to find a practical side project using machine learning and/or data science that could add value at work, but for the life of me I can't come up with any problems that I couldn't solve with a relational database (postgres) and a data transformation step.”

Permalink Hacker News

Research #llm 👥 CommunityAnalyzed: Jan 4, 2026 07:18

A Neural Network in 11 Lines of Python (2015)

Published:Oct 18, 2017 11:59

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1 min read

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Hacker News

Analysis

This article likely discusses a simplified implementation of a neural network using Python, focusing on brevity and educational value. The year 2015 suggests it might be an early example or a demonstration of fundamental concepts rather than a state-of-the-art model.

Key Takeaways

•Demonstrates the core principles of neural networks in a concise manner.
•Suitable for educational purposes and understanding the basics.
•Likely uses basic libraries like NumPy.

Reference

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Permalink Hacker News

SmallPebble: Revolutionizing Deep Learning with a Minimalist Approach

Analysis

Key Takeaways

Crafting AI Brilliance: Python Powers a Tic-Tac-Toe Master!

Analysis

Key Takeaways

Deep Dive into LLMs: A Programmer's Guide from NumPy to Cutting-Edge Architectures

Analysis

Key Takeaways

NumPy Deep Learning Log 6: Mastering Multidimensional Arrays

Analysis

Key Takeaways

Controlling LLM Output Variation: An Empirical Look at Temperature, Top-p, Top-k, and Repetition Penalty

Analysis

Key Takeaways

NumPy Fundamentals: A Beginner's Deep Learning Journey

Analysis

Key Takeaways

SmolML: A Machine Learning Library from Scratch in Python (No NumPy, No Dependencies)

Analysis

Key Takeaways

How I Cracked an AI Engineer Role

Analysis

Key Takeaways

Understanding Tensor Data Structures with Go

Analysis

Key Takeaways

NumPy Implementation of Llama 3: A Novel Approach

Analysis

Key Takeaways

Building a CNN from Scratch with NumPy: A Deep Dive

Analysis

Key Takeaways

Incorporating Machine Learning into a Shipping Company's Day Job

Analysis

Key Takeaways

A Neural Network in 11 Lines of Python (2015)

Analysis

Key Takeaways

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