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Build a Modern Computer from First Principles: From Nand to Tetris (Project-Centered Course)
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Discover 機械学習システム構築の基礎、前処理から評価まで包括的に学び、外れ値、ハイパーパラメータ、F1スコアなど重要な概念を実践的なデータで理解する90分の入門講座。
Dive into LLMOps with AWS, mastering production-ready generative AI applications through comprehensive architecture, evaluation methods, RAG implementation, fine-tuning techniques, and security best practices.
Discover AWS의 생성형 AI 애플리케이션 구축 옵션과 Amazon SageMaker JumpStart의 파운데이션 모델을 활용한 실용적인 구현 방법을 통해 AI 개발 역량을 강화하세요.
Dive into 확산 모델의 개념과 실제 적용을 통해 이미지 생성 AI의 비즈니스 가치를 파악하고, Stable Diffusion과 Amazon Bedrock을 활용한 실무 구현 방법을 습득하세요.
Discover how to build generative AI applications on AWS using SageMaker JumpStart, Bedrock, and Canvas - from foundational concepts to practical implementations for both developers and analysts.
Delve into Amazon Q Developer's capabilities to enhance software development productivity, from design to deployment, using AI-powered assistance for efficient AWS application building.
Discover machine learning fundamentals and explore Amazon SageMaker Canvas for no-code ML model development, featuring real Indonesian case studies and practical applications across various industries.
Delve into generative AI and diffusion models for image creation, covering real-world applications, fine-tuning techniques, and practical evaluations using AWS tools and Stable Diffusion.
Dive into LLMOps with AWS, mastering development and deployment of Large Language Models through hands-on examples, evaluation frameworks, RAG techniques, fine-tuning approaches, and security best practices.
Discover practical applications and business value of generative AI through industry case studies, from customer experience enhancement to workflow optimization and content creation.
Master AWS机器学习工程师助理级认证考试要点,通过实践样题和深入讲解,掌握从数据准备到模型部署的关键知识,为MLA-C01认证做好充分准备。
Dive into responsible AI principles and practices, focusing on ethical development, fairness, privacy, and transparency in generative AI systems, with hands-on AWS tools implementation guidance.
生成AIの文脈で責任あるAIの基本原則を学び、公平性、説明可能性、プライバシー、ガバナンスなどの重要な要素を理解し、AWSでの実践的な実装方法を習得する。
探索負責任 AI 的核心概念,從公平性到透明度,掌握在生成式 AI 環境中實施道德和可信賴 AI 系統的最佳實務,並了解 AWS 相關工具的應用。
책임감 있는 AI의 핵심 원칙과 AWS 도구를 활용하여 윤리적이고 투명한 AI 시스템을 구축하는 방법을 탐구하며, 공정성, 설명 가능성, 개인정보 보호에 대해 학습합니다.
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