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Generative AI courses

1093 Courses

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California Institute of Technology AWS Skill Builder

AWS Flash - Unleashing Innovation: The Generative AI Revolution (Indonesian)

Kita berada dalam masyarakat di mana garis antara apa yang diciptakan oleh manusia, dan apa yang diciptakan oleh mesin, makin kabur. AI generatif telah menjadi titik balik dalam cara kita membuat, merancang, dan berinteraksi dengan teknologi. Tetapi bagaimana cara kerjanya, apa manfaat di luar kebaruan dan apa risikonya? Bergabunglah dengan kam.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

AWS Flash - Operationalize Generative AI Applications (FMOps/LLMOps) (Simplified Chinese)

本课程概述了生产 LLM 所面临的挑战以及一套可用于解决这些挑战的工具。课程将概述开发、部署和实施 LLM 的参考架构，并展开介绍该过程的每个阶段。 课程级别：中级 时长：90 分钟 课程内容 本课程包括演讲、真实示例和案例研究。 课程目标 在本课程中，您将学习以下内容： 区分 MLOPs 和 LLMOPs，定义实施 LLM 时面临的核心挑战 学习如何为给定的使用案例.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

AWS Flash - Introduction to Responsible AI (Simplified Chinese)

本课程概述了什么是负责任的 AI 以及它在生成式人工智能背景下的重要性。负责任的 AI 是指以合乎伦理、透明、公平且负责任的方式开发、部署和使用 AI。本课程涵盖负责任的 AI 的核心维度以及建立公平性、可解释性、隐私、稳健性、监管和透明度方面的最佳实践。本课程还将介绍用于在 AWS 上负责任地构建 AI 的服务和工具。 课程级别：基础级 时长：60-75 分钟 注.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

AWS Flash - Introduction to Responsible AI (Korean)

In this course, we briefly explain what responsible AI is and why it is important in generative AI. Responsible AI refers to developing, deploying, and using AI in ethical, transparent, fair, and accountable ways. The course covers key elements of responsible AI including fairness, explainability, privacy, robustness, governance, and transparen.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

AWS Flash - Generative AI in Action: Real-World Use Cases (Indonesian)

Kursus ini memberikan gambaran umum tentang kasus penggunaan AI generatif dan nilai bisnis yang diberikan. Termasuk aplikasi dunia nyata untuk AI generatif di seluruh industri utama dan studi kasus. Tingkat kursus: Dasar Durasi: 75 menit Catatan: Kursus ini memiliki transkrip/subtitle lokal. Narasi disampaikan dalam bahasa Inggris. Untuk.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

AWS ML Engineer Associate Curriculum Overview (Korean)

AWS ML 엔지니어 어소시에이트 교육 과정의 이 입문자용 과정에서는 기계 학습(ML) 관련 기본 사항을 복습하고 ML 및 AI의 발전 상황을 살펴봅니다. 비즈니스 목표를 파악하고 ML 문제를 공식화하며 Amazon SageMaker에 대해 알아봅니다. 과정 수준: 고급 소요 시간: 45분 참고: 이 과정의 동영상에는 한국어 트랜스크립트 또는 자막이 지원됩니다. 자막을 표시하.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

Optimizing Foundation Models (Traditional Chinese)

在本課程中，您將探索兩種改善基礎模型 (FM) 效能的技術：檢索增強生成 (RAG) 和微調。您將了解 Amazon Web Services (AWS) 服務，這些服務有助於使用向量資料庫儲存內嵌、客服在多步驟任務中的角色、定義微調 FM 的方法、如何準備用於微調的資料等等。 課程等級：基礎 課程時長：1 小時 本課程包括互動元素、文字指令和說明性圖形。 課程目標 識別有助於使用向量.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Simplified Chinese)

本课程概述了 Amazon Q，这是一款基于生成式人工智能 (AI) 的助手。您将了解将 Amazon Q 与贵公司信息、代码和系统相关联的使用案例和益处。您还将根据您对特定使用案例的兴趣查找其他信息，以推进您的学习之旅。技术型和非技术型学员都将了解 Amazon Q 如何以安全可靠的方式提高其生产力。 课程级别：基础级 时长：15 分钟 注意：本课程具有本地化的注释/字幕。 旁白保留.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Traditional Chinese)

本課程提供 Amazon Q 的高階概觀，這是生成式人工智慧 (AI) 支援助理。您將會了解將 Amazon Q 連結至公司資訊、程式碼和系統的使用案例和優點。您也可以找到其他資訊，以根據您對特定使用案例的興趣推進學習旅程。技術和非技術學習者都會了解 Amazon Q 如何以安全可靠的方式提高他們的生產力。 課程等級：基礎 持續時間：15 分鐘 注意：本課程具有本地化的註釋/字幕。.
provider AWS Skill Builder
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California Institute of Technology AWS Skill Builder

BI generativa com o Amazon Q no Quicksight: introdução (Português) | Generative BI with Amazon Q in Quicksight - Getting Started (Portuguese)

O Amazon Q no QuickSight traz um conjunto inovador de recursos de business intelligence (BI) ao integrar grandes modelos de linguagem (LLMs) do Amazon Bedrock com as capacidades do Amazon QuickSight. Durante este curso, você explorará conceitos técnicos e entenderá os benefícios de utilizar o Amazon Q no QuickSight. Descubra a arquitetura e os recu.
provider AWS Skill Builder

A generative ai course is a fast-growing field of machine learning that can create new content, translate languages, write different types of creative content, and answer your questions in an informative way. It has great potential to revolutionize the way we create and use products.

A generative ai course refers to any artificial intelligence model that generates new data, information, or documents.

For example, many companies record their meetings, both live and virtual. Here are a few ways generative AI could transform these recordings:

And this is only a small part of all processes.

Generative AI Model Examples

There are a number of products using generative ai courses already available on the market – we'll give you a few examples below. The underlying principle of the generative ai courses at AI Eeducation varies depending on the specific model or algorithm used, but some common approaches include:

  1. Variational Autoencoders (VAEs) are a type of generative model that learns to encode input data into a latent space and then decode it back into the original data. The "variational" part of the name refers to the probabilistic nature of the latent space, allowing the model to generate a variety of outputs.

  2. Generative Adversarial Networks (GaN): GaNs consist of two neural networks, a generator and a discriminator, that are trained simultaneously through adversarial learning. The generator creates new data, and the discriminator evaluates how well the generated data matches the real data. The competition between the two networks causes the generator to improve over time in producing realistic outputs.

  3. Recurrent Neural Networks (RNNS) and Long Short-Term Memory (LSTM): These types of neural networks are often used to generate sequences such as text or music. RNNS and LSTM have memory that allows them to process a series of events over time, making them suitable for tasks where the order of elements is important.

  4. Transformer models: Transformer models, especially those with attention mechanisms, are very successful in various generative tasks. They can remember long-term dependencies and relationships in data, making them effective for tasks such as language translation and text generation.

  5. Autoencoders: Autoencoders consist of an encoder and a decoder, and they are trained to reconstruct the input data. Although they are primarily used for learning to represent and compress data, variations such as denoising autoencoders (e.g. in images) can be used for generative tasks.

An ai generative course involves feeding a model a large data set and optimizing its parameters to minimize the difference between the generated output and the real information. A model's ability to produce realistic and rich content depends on the complexity of its architecture, the quality and quantity of training data, and the optimization techniques used during training!