Discover the key differences between transfer learning and fine-tuning. Optimize your model training and leverage pre-trained models effectively. Learn ...

이 보고서에서는 scikit-learn 분류기를 저장하는 방법과 그것이 왜 중요한지 알아봅니다. 이 글은 AI 번역본입니다. 오역이 있을 경우 댓글로 알려 주세요.

이 단계별 튜토리얼에서는 Microsoft의 Azure AI 제품군과 함께 W&B Weave를 활용하는 방법을 살펴봅니다. 이 글은 AI 번역본입니다. 오역이 의심되면 댓글로 알려주세요.

Weights & Biases의 머신 러닝 엔지니어 || Google Developer Expert (JAX) 이 글은 AI 번역본입니다. 오역이 있으면 댓글로 알려주세요.

새로운 커뮤니티 이벤트에 참여하고 싶으신가요? 이렇게 시작하세요! 이 글은 AI 번역본입니다. 오역이 의심되면 댓글로 알려주세요.

이 글에서는 AI 시스템에서의 프롬프트 인젝션 공격을 이해하고, 이를 효과적으로 방지하기 위한 전략을 살펴보겠습니다! 이 글은 AI 번역본입니다. 오역이 있을 수 있으니 댓글로 자유롭게 알려 주세요.

This is an AI translated article. Feel free to report any possible mis-translations in the comments section

Discover how to fine-tune Gemma 3 270M for Python code using LoRA. Boost code generation and understanding with efficient methods, affordable hardware, ...

이 글은 AI가 번역한 기사입니다. 오역이 의심되면 댓글로 알려 주세요.

Learn to fine-tune the Gemma 3 270M model for Python using LoRA, a parameter-efficient method that reduces computational load while enhancing performance.

Learn to fine-tune the Gemma 3 270M model for Python code generation efficiently using Low-Rank Adaptation, reducing costs while boosting performance.

Fine-tune Gemma 3 270M for Python code with LoRA to enhance performance and adaptability. This guide shows you how to efficiently train models for niche...

Learn how to fine-tune the Gemma 3 270M model on Python code using LoRA for efficient performance. Enhance LLM utility with reduced costs and hardware d...

Discover how to fine-tune Gemma 3 270M for Python code using LoRA, enhancing task performance and efficiency with reduced costs. Master LLM finetuning t...

Discover how to fine-tune Gemma 3 270M for Python code with LoRA and W&B. Enhance model performance efficiently while reducing computational demands usi...

Master LLM finetuning to enhance Python code tasks, boosting model accuracy and relevance. Learn to adapt pre-trained models for efficient, targeted sol...

Unlock the potential of Gemma 3 270M by fine-tuning it on Python code with LoRA. Learn efficient methods to enhance LLM performance while reducing compu...

Fine-tune Gemma 3 270M for Python code efficiency, enhancing its task performance with parameter-efficient methods and W&B tools. Improve code quality a...

Discover efficient techniques for fine-tuning Gemma 3 270M on Python code tasks. Learn to boost performance with LoRA, cutting costs while enhancing mod...

Discover how to fine-tune Gemma 3 270M for Python code using efficient methods like LoRA and instruction tuning, enhancing LLM performance without high ...

Fine-tune Gemma 3 270M for Python code using LoRA to enhance accuracy with minimal resources. Learn step-by-step with W&B tools for seamless tracking.

Master Python code adaptation by fine-tuning Gemma 3 270M! Learn cutting-edge techniques like LoRA and instruction tuning with Weights & Biases. Start t...

강화학습이 시행착오를 통해 AI가 학습하도록 돕는 방식과 핵심 알고리즘, RLHF와 같은 방법, 그리고 실제 적용 사례를 살펴보세요. 이 글은 AI 번역본입니다. 오역이 의심되면 댓글로 알려주세요.

LLaVA-o1가 구조화된 문제 해결 방식으로 멀티모달 AI의 추론 과제를 어떻게 해결하는지 알아보세요. 데이터셋, 기능, 그리고 W&B Weave를 활용한 성능 분석을 소개합니다. 이 글은 AI 번역본입니다. 오역이 있을 경우 댓글로 알려주세요.

Amazon Bedrock과 W&B Weave를 함께 활용하여 요약 작업에 적합한 대규모 언어 모델(LLM)을 평가·비교하는 방법을 알아보세요. Bedrock의 관리형 인프라와 Weave의 고급 평가 기능을 결합해 효율적으로 벤치마크하고 성능을 분석할 수 있습니다. 이 글은 AI 번역본입니다. 오역이 있을 경우 댓글로 알려주세요.

Google Cloud와의 최신 파트너십에서 알아두어야 할 사항 이 글은 AI로 번역되었습니다. 오역이 의심되면 댓글로 알려주세요.

Google の GenAI SDK は、最新の Gemini モデルを用いてテキスト、画像、動画などの高度な生成 AI 機能をアプリケーションにシームレスに統合できる、統一的で柔軟なツールキットを開発者に提供します。この記事は AI による翻訳です。訳抜けや不自然な箇所があればコメント欄でお知らせください。

Ceci est un article traduit par une IA. N'hésitez pas à signaler d'éventuelles erreurs de traduction dans la section des commentaires.

This is an AI translated article. Feel free to report any possible mis-translations in the comments section

This is an AI translated article. Feel free to report any possible mis-translations in the comments section

This is an AI translated article. Feel free to report any possible mis-translations in the comments section

CrewAI と W&B Weave で、AI エージェントの構築とデバッグをより迅速に。マルチエージェントワークフローのあらゆるステップを監視・分析し、最適化できます。この記事は機械翻訳版です。誤訳の可能性があればコメント欄でお知らせください。

CrewAI によるマルチエージェント連携と Weave を用いたリアルタイムのデバッグと可観測性を備えた、完全自動の GitHub ドキュメント生成システムを構築するための実践ガイド。この記事は AI による翻訳です。誤訳の可能性があれば、コメント欄でお知らせください。

Google の GenAI SDK は、最新の Gemini モデルを用いてテキスト・画像・動画などの高度な生成 AI 機能をアプリケーションにシームレスに統合できる、統一的で柔軟な開発者向けツールキットです。この記事は AI による翻訳です。誤訳の可能性があればコメント欄でお知らせください。

Claude 4 Sonnet と Opus を徹底検証 これは機械翻訳された記事です。誤訳の可能性があればコメント欄でご指摘ください。

本稿では、光学式文字認識（OCR）を取り上げ、学習済みのテキスト検出（ローカライゼーション）と認識モデルを活用して、画像からテキストを見つけて抽出する方法を解説します。

効率的な機械学習実験のためのアーティファクト参照の徹底解説 本記事は翻訳版です。誤訳の可能性があればコメント欄でお知らせください。

本記事では、学生、学術研究室、研究グループ、学生団体などを対象に、教育分野におけるクラウドコンピューティングの可能性—特に GPU の活用—を紹介します。この記事は元記事の翻訳版です。誤訳の可能性があれば、コメント欄でお知らせください。

本記事では、Weights & Biases を用いて、探索的データ分析を数分以内で手軽に共有できる方法を紹介します。これは記事の翻訳版です。誤訳の可能性があれば、コメント欄でお知らせください。

機械学習でよく使われる関数の比較解説 この記事は翻訳版です。訳の不備や誤訳がありましたら、コメント欄でお知らせください。

本記事では、ResNet がどのように、そしてなぜ機能するのかを学び、さらに自分で構築する方法を解説します。PyTorch と PyTorch Image Models（TIMM）を用いて ResNet モデルを実装します。訳文に関する誤りがあれば、コメント欄でお知らせください。

本記事では、Weights & Biases を用いて、より小規模な ImageNet 風データセットにおける EfficientNet ファミリーの評価を行います。この記事は翻訳版です。誤訳などがあればコメント欄でお知らせください。

Weave を使って API 呼び出しをトレース・管理し、LLM を最大限に活用する この記事は翻訳版です。訳語の誤りなどがあれば、コメント欄でお知らせください。

機械学習でよく使われる関数を比較する解説記事です。この記事は翻訳版です。誤訳に気付かれた場合はコメント欄で報告してください。

この記事では、Weights & Biases を使って、より小規模な ImageNet に似たデータセット上で EfficientNet ファミリを評価します。これはこの記事の翻訳版です。誤訳があればコメント欄でお知らせください。

LEARN HOW TO COMPARE AND EVALUATE OPENAI’S GPT MODELS ON AZURE WITH W&B WEAVE ON TEXT SUMMARIZATION TASKS, LEVERAGING AZURE’S MANAGED INFRASTRUCTURE AND WEAVE’S CUSTOMIZABLE EVALUATION TOOLS. THIS IS A TRANSLATED VERSION OF THE ARTICLE. FEEL FREE TO REPORT ANY POSSIBLE MIS-TRANSLATIONS IN THE COMMENTS SECTION This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

LEARN HOW TO COMPARE AND EVALUATE OPENAI’S GPT MODELS ON AZURE WITH W&B WEAVE ON TEXT SUMMARIZATION TASKS, LEVERAGING AZURE’S MANAGED INFRASTRUCTURE AND WEAVE’S CUSTOMIZABLE EVALUATION TOOLS. THIS IS A TRANSLATED VERSION OF THE ARTICLE. FEEL FREE TO REPORT ANY POSSIBLE MIS-TRANSLATIONS IN THE COMMENTS SECTION This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

W&B Weave を使って、Azure の管理されたインフラと Weave のカスタマイズ可能な評価ツールを活用しながら、テキスト要約タスクにおける OpenAI の GPT モデルを比較・評価する方法を学びます。この記事は翻訳版です。誤訳の可能性があればコメント欄でお知らせください。

LEARN HOW TO COMPARE AND EVALUATE OPENAI’S GPT MODELS ON AZURE WITH W&B WEAVE ON TEXT SUMMARIZATION TASKS, LEVERAGING AZURE’S MANAGED INFRASTRUCTURE AND WEAVE’S CUSTOMIZABLE EVALUATION TOOLS. THIS IS A TRANSLATED VERSION OF THE ARTICLE. FEEL FREE TO REPORT ANY POSSIBLE MIS-TRANSLATIONS IN THE COMMENTS SECTION This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A new set of Qwen models trained on a massive dataset! This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

An overview of popular datasets used for ML in finance! This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A guide for making the most out of your GPU's! This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A battle between Open Source Pixtral Large and closed source foundation models like Claude 3.5 Sonnet and GPT-4o Vision This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Explore how reinforcement learning helps AI learn from trial and error, with key algorithms, methods like RLHF, and real-world applications. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

We will focus on understanding prompt injection attacks in AI systems and explore effective strategies to prevent against them! This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Discover how LLaVA-o1 tackles reasoning challenges in multimodal AI with structured problem-solving. Learn about its dataset, capabilities, and performance analysis using W&B Weave. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Discover how to use Amazon Bedrock in combination with W&B Weave to evaluate and compare Large Language Models (LLMs) for summarization tasks, leveraging Bedrock’s managed infrastructure and Weave’s advanced evaluation features.

 This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Discover how to use Amazon Bedrock in combination with W&B Weave to evaluate and compare Large Language Models (LLMs) for summarization tasks, leveraging Bedrock’s managed infrastructure and Weave’s advanced evaluation features.

 This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Discover how to use Amazon Bedrock in combination with W&B Weave to evaluate and compare Large Language Models (LLMs) for summarization tasks, leveraging Bedrock’s managed infrastructure and Weave’s advanced evaluation features.

 This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Learn how to enforce consistency on GPT-4o outputs, and build reliable Gen-AI Apps. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A guide to building a RAG system with DSPy, and evaluating it with W&B Weave. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Building a AI powered coding agent with Claude 3.5 Sonnet! This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

How to use one of the worlds fastest and most accurate object detectors to run inference, display on your webcam using OpenCV and tracking your results. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

How to use one of the worlds fastest and most accurate object detectors to run inference, display on your webcam using OpenCV and tracking your results. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Here's how to get up and running with the newest model from Anthropic This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Retrieval Augmented Thinking (RAT) separates AI reasoning from response generation, improving efficiency, interpretability, and customization by using one model for structured thought and another for the final output. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This guide explores how AI agents, powered by CrewAI, automate complex tasks with minimal human input by integrating adaptive workflows, real-time data analysis, and iterative improvements. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Can fine-tuning and budget forcing improve GPT-4o’s reasoning? We test structured datasets and inference-time techniques to boost multi-step problem-solving. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This guide explores how MCP standardizes AI interactions with external tools and data sources, enabling more efficient AI context integrations. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This guide explores how MCP standardizes AI interactions with external tools and data sources, enabling more efficient AI context integrations. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Learn how to build a Reddit sentiment analysis pipeline that uses GPT-4o-mini to extract opinions from real discussions across subreddits—filtering, summarizing, and classifying posts and comments at scale. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Deploy Llama 4 locally or via API with Python scripts. We test multimodal performance against GPT-4o on ChartQA and show how to debug and compare results using Weave. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Learn how autonomous AI agents automate tasks with minimal supervision, their architecture, applications, risks, and how to build a HackerNews AI news reporter. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Learn how to set up and run OpenAI o3-mini via the API, explore its flexible reasoning effort settings, and compare its performance against DeepSeek-R1 using W&B Weave Evaluations. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Learn to build and monitor powerful AI agents with Amazon Bedrock and W&B Weave for automated workflows. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

The Agent2Agent (A2A) protocol is an open standard that enables autonomous AI agents to securely discover, communicate, and collaborate across platforms. Learn how it works, its core components, and how to implement it. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article introduces GraphRAG, a novel approach that combines knowledge graphs and hierarchical community detection to enable scalable, query-focused summarization and global sensemaking over large datasets. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A practical walkthrough for building OpenAI Agents that use the Model Context Protocol (MCP) to access tools, files, and trace data via Weave. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This project explores multi-agent AI systems, examining how multiple specialized agents collaborate to enhance decision-making, problem-solving, and automation across various domains. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Gemini 2.5 Pro Experimental is Google's most advanced AI model to date, featuring multimodal input support, a massive 1 million-token context window, and the ability to solve complex problems. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A real-world head-to-head test of Gemini 2.5 Pro, o4-mini, and Claude 3.7 Sonnet on competitive programming problems—built on a custom execution framework with Weave integration to track correctness, spot bugs, and cut through benchmark hype. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Experimenting with Anthropic's new flagship LLM, Claude 3.7 Sonnet! This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Discover the capabilities of DeepSeek-R1 and OpenAI o1 models for reasoning and decision-making. Includes setup guides, API usage, local deployment, and Weave-powered comparisons. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A hands-on guide to building and evaluating real-time, tool-using AI agents with Azure AI Foundry Agent Service, SerpAPI, and W&B Weave. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A hands-on guide to building and evaluating real-time, tool-using AI agents with Azure AI Foundry Agent Service, SerpAPI, and W&B Weave. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

We test whether budget forcing - a simple test-time intervention - can significantly boost the reasoning accuracy of s1-32B, potentially enabling smaller models to rival closed-source giants like OpenAI's o1-preview. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Learn how autonomous AI agents automate tasks with minimal supervision, their architecture, applications, risks, and how to build a HackerNews AI news reporter. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article explores the ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metric, a powerful tool used for evaluating the quality of AI-generated text This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article breaks down BLEU, a key metric for evaluating machine-generated text, covering its mechanics, practical applications with Python and Weave, and its role in improving text generation systems. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article highlights the importance of PII, detection methods like regex, Presidio, and transformers, and evaluation with Weave to ensure accurate and adaptable data protection. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article explores the challenges of detecting and managing toxicity in AI systems, providing actionable strategies and tools to foster safer and more inclusive digital interactions. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Robustness evaluates how consistently large language models perform under noisy or perturbed inputs, using statistical metrics like Cohen’s d to quantify their reliability and adaptability in real-world applications. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article explores relevance scoring in AI, detailing tools, datasets, and methods for evaluating and refining how well model outputs align with input prompts and context. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Coherence, a measure of clarity and logical consistency in AI-generated responses, is effectively evaluated and refined using Weave's comprehensive tools and comparison insights. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article explores bias in AI systems, the need for bias guardrails, detection models, and strategies to mitigate, monitor, and evaluate bias effectively. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article explores how AI agents are transforming retail by automating customer interactions, optimizing decision-making, and enhancing product recommendations using LLM-driven vector search. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Explore AI agent workflows for automating tasks with multi-agent systems and generative AI, including a tutorial to build a research assistant for AI summaries. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article explores how agentic RAG enhances retrieval-augmented generation by using AI agents to dynamically refine search strategies, coordinate multiple data sources, and improve response accuracy. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Google’s GenAI SDK provides developers with a unified, flexible toolkit to seamlessly integrate advanced generative AI capabilities—including text, image, and video processing—into their applications using the latest Gemini models. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Google’s GenAI SDK provides developers with a unified, flexible toolkit to seamlessly integrate advanced generative AI capabilities—including text, image, and video processing—into their applications using the latest Gemini models. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article shows how exploratory data analyses can be made easily shareable in less than a few minutes with Weights & Biases. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article shows how exploratory data analyses can be made easily shareable in less than a few minutes with Weights & Biases.

Build and debug AI agents faster with CrewAI and W&B Weave. Monitor, analyze, and optimize every step of your multi-agent workflows. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Putting Claude 4 Sonnet and Opus to the test This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

These tutorials cover how to evaluate GPT-5’s image generation, coding evals, and automated debugging using W&B Weave. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Getting set up and running Anthropic's new Claude 4 Sonnet and Opus on your machine in Python using the API. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article explains how Blue River Technology uses PyTorch and Weights & Biases on industrial weeding robots in fields across America This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this article, we explore Reinforcement Learning from Human Feedback, a novel approach to reducing bias and increasing performance in large language models. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this article, we explore Reinforcement Learning from Human Feedback, a novel approach to reducing bias and increasing performance in large language models. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this report, we will look at the latest work published in CVPR 21 in the domain of one-shot talking-head synthesis. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this article, we'll do a quick comparison of the benefits and detriments of two different ways to downscale input tensor: pooling and strided convolutions. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Simple answers to common questions related to the Keras layer arguments, including input shape, weight, units and dim. With examples. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this tutorial, we walk you through how to check if PyTorch is using your GPU. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Demystify the View function in PyTorch and find a better way to design models. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Demystify the View function in PyTorch and find a better way to design models. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this article, we'll be exploring the Woven Planet (Lyft) Level 5 dataset. We'll look at what it is as well as the autonomous vehicle tasks and techniques it supports This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

The Waymo Open Dataset is a perception and motion planning video dataset for self-driving cars. It’s composed the perception and motion planning datasets. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Semantic-Kitti is a large semantic segmentation and scene understanding dataset developed for LiDAR-based autonomous driving. But what it is and what is it for? This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

PandaSet is a high-quality autonomous driving dataset that boasts the most number of annotated objects among 3d scene understanding datasets. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

nuScenes is a large-scale 3D perception dataset for Autonomous Driving provided by motional. The dataset has 3D bounding boxes for 1000 scenes. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

The BDD100K dataset is the largest and most diverse driving video dataset with 100,000 videos annotated for 10 different perception tasks in autonomous driving. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

The BDD100K dataset is the largest and most diverse driving video dataset with 100,000 videos annotated for 10 different perception tasks in autonomous driving. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

nuScenes is a large-scale 3D perception dataset for Autonomous Driving provided by motional. The dataset has 3D bounding boxes for 1000 scenes. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This report demonstrates how to use Stock News API and FinBERT for the Numerai Signals tournament This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this article, we will explore the work presented in the paper "Rewriting a Deep Generative Model" by Bau et al. It shows a new way of looking at deep neural networks. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

This article provides an overview of DeepFaceDrawing, breaking down the key concepts and diving into this image-to-image translation technique. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

As AI translation evolves and makes our lives easier, many questions arise about its impact and future potential. This article gives the essentials you need to know. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Real-time collaboration and publication-ready graphics with a few mouse clicks or lines of code This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Real-time collaboration and publication-ready graphics with a few mouse clicks or lines of code This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this article, we learn how—and why—ResNets work and discover how to build our own. We implement a ResNet model using PyTorch and PyTorch Image Models (TIMM).

In this article, we learn how—and why—ResNets work and discover how to build our own. We implement a ResNet model using PyTorch and PyTorch Image Models (TIMM). This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this article, we learn how—and why—ResNets work and discover how to build our own. We implement a ResNet model using PyTorch and PyTorch Image Models (TIMM).

In this article, we explore optical character recognition and leverage pre-trained text localization and recognition models to find and extract text from images. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this article, we explore the potential of cloud computing in eduction, including GPUs for students, academic labs, research groups, student-run orgs, and more. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

A Deep Dive into Artifact Referencing for Streamlined Machine Learning Experimentation This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Getting the most out of your LLMs by using Weave to trace and manage your API calls This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Develop techniques for building, optimizing, and scaling AI evaluators with minimal human input. Learn to build reliable evaluation pipelines for LLM applications by combining programmatic checks with LLM-based judges. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

On 2  June  2025 we opened the doors to our newest course entitled AI Engineering: Agents. If you’ve ever wondered how to get from a reliable prompt chain to a fleet of autonomous, memory‑aware GPT agents, this course is for you. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Explore advanced RAG techniques like hybrid search and LLM context management through a case study and hands-on exercises. Included in the course are code notebooks and Cohere credits to build and experiment with your own RAG systems. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

We're pleased to offer a new, free course on LLM prompting that covers everything from prompt anatomy to advanced techniques. We'd love it if you gave it a try. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Can you rank LLMs through debate? That's precisely what we're trying to find out. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Use W&B Artifacts to make your teams' models auditable This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Use W&B Artifacts to make your teams' models auditable This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Automatic Speech Recognition (ASR) refers to automatically transcribing spoken language, otherwise known as speech-to-text. In this blog, you will learn how to use NVIDIA’s Neural Modules (NeMo) toolkit to train an end-to-end ASR system and Weights & Biases to keep track of various experiments and performance metrics. This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this report, you'll learn how to save a sci-kit learn classifier and why it's important in the first place This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

In this step-by-step tutorial, we'll look at how W&B Weave alongside Microsoft's suite of Azure AI offerings This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Machine Learning Engineer at Weights & Biases || Google Developer Expert (JAX) This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Want to join our newest community event? Here's how! This is a translated version of the article. Feel free to report any possible mis-translations in the comments section

Weights & BiasesのMachine Learning Engineer｜Google Developer Expert（JAX） この記事は翻訳版です。誤訳の可能性があれば、コメント欄でお知らせください。

Machine Learning Engineer at Weights & Biases || Google Developer Expert (JAX)

Machine Learning Engineer at Weights & Biases || Google Developer Expert (JAX)

Machine Learning Engineer at Weights & Biases || Google Developer Expert (JAX)

自動音声認識（ASR、音声→テキスト）とは、話し言葉を自動で文字起こしする技術です。本記事では、NVIDIA NeMo ツールキットを用いてエンドツーエンドの ASR システムを訓練し、Weights & Biases（W&B）で実験管理と評価指標の追跡を行う方法を解説します。これは記事の翻訳版です。誤訳などがあればコメント欄でお知らせください。

[NOTE: This is a translated version of the article.] 自動音声認識（ASR）は、話し言葉を自動的に文字起こしする、いわゆる音声からテキストへの変換を指します。本記事では、NVIDIAのNeMo（NVIDIA NeMo）ツールキットを用いてエンドツーエンドのASRシステムを訓練し、Weights & Biasesを使って各種実験や性能指標を追跡する方法を学びます。

Automatic Speech Recognition (ASR) refers to automatically transcribing spoken language, otherwise known as speech-to-text. In this blog, you will learn how to use NVIDIA’s Neural Modules (NeMo) toolkit to train an end-to-end ASR system and Weights & Biases to keep track of various experiments and performance metrics.

自動音声認識（ASR）は、音声を自動的に文字起こしする技術で、いわゆるスピーチ・トゥ・テキストを指します。本記事では、NVIDIA の Neural Modules（NeMo）ツールキットを使ってエンドツーエンドの ASR システムを訓練する方法と、Weights & Biases を用いて各種実験や性能指標を追跡・管理する方法を解説します。

自動音声認識（ASR）は、話された言語を自動的に文字に変換する、いわゆる音声→テキストのことを指します。本記事では、NVIDIA の NeMo ツールキットを使ってエンドツーエンドの ASR システムを訓練する方法と、Weights & Biases を用いて各種実験や性能指標を追跡する方法を解説します。

自動音声認識（ASR）は、音声を自動的に文字起こしする技術（スピーチ・トゥ・テキスト）を指します。本記事では、NVIDIA の NeMo ツールキットを使ってエンドツーエンドの ASR システムを学習させ、Weights & Biases で実験や性能指標を記録・管理する方法を解説します。

自動音声認識（ASR）は、話された言語を自動的に文字起こしする技術、つまり音声→テキストを指します。本記事では、NVIDIA の NeMo ツールキットを使ってエンドツーエンドの ASR システムを学習させ、Weights & Biases を使って実験や性能指標を追跡する方法を説明します。

自動音声認識（ASR）は、人間の発話を自動で文字起こしする技術であり、いわゆる音声からテキスト変換（speech-to-text）を指します。本記事では、NVIDIA Neural Modules（NeMo）ツールキットを用いてエンドツーエンドのASRシステムを学習し、Weights & Biasesで実験や各種指標を記録・管理する方法を解説します。

Automatic Speech Recognition (ASR) refers to automatically transcribing spoken language, otherwise known as speech-to-text. In this blog, you will learn how to use NVIDIA’s Neural Modules (NeMo) toolkit to train an end-to-end ASR system and Weights & Biases to keep track of various experiments and performance metrics.

Automatic Speech Recognition（ASR）は、音声を自動で文字起こしする、いわゆる音声からテキストへの変換を指します。本記事では、NVIDIA の Neural Modules（NeMo）ツールキットを使ってエンドツーエンドの ASR システムを学習する方法と、実験や性能指標の追跡に Weights & Biases を使用する方法を説明します。

自動音声認識（ASR）は、話し言葉を自動で文字に変換する技術、いわゆるスピーチ・トゥ・テキストを指します。本記事では、NVIDIAのNeMo（Neural Modules）ツールキットを使ってエンドツーエンドのASRシステムを学習させる方法と、実験や性能指標の追跡にWeights & Biasesを利用する方法を紹介します。

このレポートでは、scikit-learnの分類器をどのように保存するか、そしてそもそも保存がなぜ重要なのかを説明します。

このステップバイステップのチュートリアルでは、Weights & Biases の Weave と Microsoft の Azure AI 製品群を組み合わせてどのように利用するかを説明します

最新のコミュニティイベントに参加しませんか？参加方法はこちらです。

クロスエントロピー損失を解説するチュートリアル。PyTorch と TensorFlow でクロスエントロピー損失関数を実装するコード例と、対話的な可視化を含みます。

本記事では、Hugging Face Transformers のハイパーパラメータ最適化について、3 つの戦略を解説し、実験の追跡には Weights & Biases（W&B）を用います。

本記事は、PyTorch でモデルを保存・読み込む方法を解説する機械学習チュートリアルであり、バージョン管理には Weights & Biases を用います。

コードチュートリアルとインタラクティブな可視化付きで、PyTorchモデルをDropoutで正則化する方法を学ぶ

PyTorchで深層学習モデルにGPUを使用するための短いチュートリアルです。GPUの利用可能性の確認から、使用可能なリソースの可視化までを説明します。

本記事では、Hugging Face Transformers のハイパーパラメータ最適化に対する3つの戦略を解説し、実験の追跡には Weights & Biases（W&B）を使用します。

クロスエントロピー損失を解説するチュートリアル。PyTorch と TensorFlow でクロスエントロピー損失関数を実装するコード例に加え、インタラクティブな可視化も含みます。

本記事では、Hugging Face Transformers のファインチューニングにおけるハイパーパラメータ最適化の3つの戦略を紹介し、実験の追跡には Weights & Biases（W&B）を用います。

この記事は、PyTorch でモデルを保存・読み込みする方法を解説する機械学習チュートリアルであり、バージョン管理には Weights & Biases（W&B）を使用します。

PyTorchモデルをDropoutで正則化する方法を学びましょう。コード付きチュートリアルとインタラクティブな可視化も含まれています。

PyTorch でディープラーニング用に GPU を使うための短いチュートリアル。利用可能かの確認から、実際に活用し可視化するところまでをカバーします。

PyTorch を使ってディープラーニングのモデルで GPU を使うための短いチュートリアルです。GPU の利用可能性の確認から、メトリクスの可視化までを扱います。

クロスエントロピー損失を解説するチュートリアル。PyTorch と TensorFlow でのクロスエントロピー損失関数の実装コードと、インタラクティブな可視化を含みます。

本記事は、PyTorch のモデルを保存・読み込みする方法を解説する機械学習チュートリアルであり、バージョン管理には Weights & Biases を使用します。

コードチュートリアルとインタラクティブな可視化付きで、PyTorchモデルをDropoutで正則化する方法を学びましょう

PyTorch でディープラーニングモデルに GPU を活用するための短いチュートリアル。GPU の利用可能性の確認から、活用状況の可視化までを扱います。

PyTorchで深層学習モデルにGPUを使用するための短いチュートリアル。利用可能性の確認から利用可能なものの視覚化まで。

この記事は、バージョン管理のためにWeights & Biasesを使用してPyTorchでモデルを保存および読み込む方法に関する機械学習チュートリアルです。

This article is a machine learning tutorial on how to save and load your models in PyTorch using Weights & Biases for version control.

This article is a machine learning tutorial on how to save and load your models in PyTorch using Weights & Biases for version control.

クロスエントロピー損失に関するチュートリアル、PyTorchとTensorflowでクロスエントロピー損失関数を実装するためのコードサンプル、インタラクティブなビジュアライゼーション。

この記事では、HuggingFace Transformersのハイパーパラメータ最適化のための3つの戦略を説明し、W&Bを使用して実験を追跡します。

PyTorchモデルをDropoutで正則化する方法を学ぶためのコードチュートリアルとインタラクティブなビジュアライゼーション。

PyTorchでディープラーニングモデルにGPUを使用するための短いチュートリアルを以下に示します。 1. **GPUの利用可能性の確認**: GPUが利用可能かどうかを確認するには、`torch.cuda.is_available()`を使用します。 2. **デバイスの設定**: モデルとデータをGPU上で実行するには、デバイスにGPUを指定します。例えば、`device = torch.device("cuda" if torch.cuda.is_available() else "cpu")`を使用します。 3. **モデルの転送**: モデルをGPUに転送するには、`.to(device)`メソッドを使用します。`model = model.to(device)` 4. **データの転送**: テンソルデータもGPUに転送します。`input = input.to(device)`のように使用します。 5. **トレーニングと評価**: GPUを使用してモデルのトレーニングや評価を行います。 6. **GPUメモリの可視化**: GPUメモリ使用量を確認するには、`torch.cuda.memory_allocated()`や`torch.cuda.memory_reserved()`を使用します。

PyTorchモデルをドロップアウトで正則化する方法を学びましょう。コードチュートリアルとインタラクティブな視覚化を含みます。

PyTorchを使用した深層学習モデル向けGPUの使用に関する簡単なチュートリアル。利用可能かどうかの確認から、使用可能なものの視覚化まで。

PyTorchでディープラーニングモデルにGPUを使用するための簡単なチュートリアルとして、利用可能かの確認から使用可能なものの可視化まで。

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

ディープラーニングモデルでGPUを使用するための短いチュートリアル：利用可能かどうかの確認から、使用可能なものの視覚化まで。

PyTorchを使用したディープラーニングモデルにGPUを利用するための簡単なチュートリアル、利用可能性の確認から使用可能なものの可視化まで。

PyTorchでディープラーニングモデルを実行するためのGPUの使用に関する短いチュートリアル。利用可能性の確認から使用可能なGPUの視覚化まで。

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

PyTorchを使用したディープラーニングモデルにおけるGPUの使用に関する短いチュートリアル：利用可能性の確認から使用可能なものの可視化まで。

PyTorchでのディープラーニングモデルのためのGPU使用方法に関する簡単なチュートリアルを紹介します。 1. **GPUの利用可能性を確認する**: - `torch.cuda.is_available()`を使用して、GPUが利用可能かどうかを確認します。 2. **モデルとデータのGPU転送**: - モデルやデータをGPUに転送するには、`.to('cuda')`メソッドを使用します。 3. **学習ループ内でのGPU使用**: - データをGPUで処理するために、入力データやラベルをGPUに転送します。 4. **GPUメモリ使用量の確認**: - `nvidia-smi`コマンドでGPUメモリの使用状況を確認できます。 5. **結果の可視化**: - 学習過程を可視化するために、GPUでの処理を考慮した方法でプロットやログを作成します。

クロスエントロピー損失をカバーするチュートリアルと、PyTorchおよびTensorFlowでクロスエントロピー損失関数を実装するコードサンプル、およびインタラクティブなビジュアライゼーション。

この記事では、HuggingFace Transformersのハイパーパラメータ最適化のための3つの戦略を、W&Bを使用して実験を追跡する方法について説明します。

この記事は、Weights & Biases を使用してモデルを保存および読み込む方法についての PyTorch の機械学習チュートリアルです。

PyTorchモデルをドロップアウトで正則化する方法を学びましょう。コードチュートリアルとインタラクティブなビジュアライゼーションも含まれています。

PyTorchでのディープラーニングモデルのためのGPU使用方法に関する簡単なチュートリアルを紹介します。 1. **GPUの利用可能性を確認する**: - `torch.cuda.is_available()`を使用して、GPUが利用可能かどうかを確認します。 2. **モデルとデータのGPU転送**: - モデルやデータをGPUに転送するには、`.to('cuda')`メソッドを使用します。 3. **学習ループ内でのGPU使用**: - データをGPUで処理するために、入力データやラベルをGPUに転送します。 4. **GPUメモリ使用量の確認**: - `nvidia-smi`コマンドでGPUメモリの使用状況を確認できます。 5. **結果の可視化**: - 学習過程を可視化するために、GPUでの処理を考慮した方法でプロットやログを作成します。

クロスエントロピー損失を解説するチュートリアル。PyTorchとTensorFlowでクロスエントロピー損失関数を実装するコードサンプルとインタラクティブな視覚化を含む。

PyTorchで深層学習モデルにGPUを使用するための簡単なチュートリアル: 1. **GPUの利用可能性を確認する**: ```python import torch torch.cuda.is_available() ``` 2. **GPUデバイスの設定**: ```python device = torch.device("cuda" if torch.cuda.is_available() else "cpu") ``` 3. **モデルのGPUへの移動**: ```python model.to(device) ``` 4. **データのGPUへの移動**: ```python inputs, labels = inputs.to(device), labels.to(device) ``` 5. **GPUメモリの確認**: ```python torch.cuda.memory_allocated() torch.cuda.memory_reserved() ``` 6. **トレーニングループでの使用**: - モデル・データはすでにGPU上にあるため、通常のトレーニングループを実行。 7. **GPUの可視化**: - NVIDIA製のGPUを使用している場合、`nvidia-smi` コマンドで利用状況をチェック可能。

PyTorchで重みを初期化する方法に関する短いチュートリアル。コードとインタラクティブな可視化付き。

PyTorchでの重みの初期化方法に関する簡単なチュートリアル。コードとインタラクティブな可視化を含む。

PyTorchで重みを初期化する方法についての短いチュートリアル。コードとインタラクティブな視覚化を含みます。

A short tutorial on how you can initialize weights in PyTorch with code and interactive visualizations.

A short tutorial on how you can initialize weights in PyTorch with code and interactive visualizations.

PyTorchでコードとインタラクティブな可視化を用いて重みを初期化する方法に関する短いチュートリアル。

PyTorchで重みを初期化する方法に関する短いチュートリアル。コードとインタラクティブな可視化付き。

PyTorchで重みを初期化する方法に関する短いチュートリアルとコード及び対話式の可視化。

A short tutorial on how you can initialize weights in PyTorch with code and interactive visualizations.

PyTorchでの深層学習モデルにGPUを使用するための簡単なチュートリアル。利用可能なGPUの確認から使用状況の可視化まで。

A short tutorial on using GPUs for your deep learning models with PyTorch, from checking availability to visualizing usable.

OpenAIは、ノルウェーのナルヴィクに位置するヨーロッパ初のAIデータセンタープロジェクト「スターゲート・ノルウェー」を発表しました。

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Este informe ha sido actualizado con nuevo contenido.

この報告書は新しい内容で更新されました。

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この報告書は新しい内容で更新されました。

このレポートは新しい内容で更新されました。

Este informe ha sido actualizado con nuevo contenido.

Este informe ha sido actualizado con nuevo contenido.

Created using only the provided functions.

この報告書は新しい内容で更新されました。

この報告書は新しい内容で更新されました。

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（じんごいんがたいせんはいちどけうちさいし）はおふみりがたつみであられあたいすはすてきです。

この報告書は、現在のデータを更新した内容で再編集されました。

インフォームेशनは今度新しいコンテンツが追加されました。

Eisu ɪnforu to desu kasedesu arate o saku shin Contentsu ni atsurageta.

This report has been updated with new content.