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Generative AI courses

659 Courses

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.
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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.
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California Institute of Technology AWS Skill Builder

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

AWS Flash: Generative AI in Action - Real-World Use Cases (Simplified Chinese) 本课程概述了生成式 AI 使用案例及其提供的商业价值。包括生成式 AI 在主要行业和案例研究中的实际应用。 课程级别：基础级 时长：75 分钟 注意：本课程具有本地化的注释/字幕。 旁白保留英语。要显示字幕，请单击播放器右下角的 CC 按钮。 课程内容 本课程包括讲解、真实示例和案例研.
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California Institute of Technology AWS Skill Builder

AWS Cloud Quest: Generative AI (Japanese)

Provider: AWS Skill Builder Categories: Machine Learning Courses, Cloud Computing Courses, Generative AI Courses, Amazon Web Services (AWS) Courses, Amazon SageMaker Courses, LangChain Courses, Amazon S3 Courses, AWS Lambda Courses
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California Institute of Technology AWS Skill Builder

AWS Cloud Quest: Machine Learning (Japanese)

Explore the dynamic world of cloud computing and machine learning with AWS Cloud Quest: Machine Learning - offered in Japanese by AWS Skill Builder. This course is your gateway to mastering essential concepts such as: Cloud computing fundamentals with Amazon S3. Introductory cloud steps using Amazon EC2 and AWS infrastructure. Estimat.
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California Institute of Technology AWS Skill Builder

AWS Flash - Introduction to Responsible AI (Traditional Chinese)

本課程提供對負責任 AI 的基本介紹，重點探討其在生成式 AI 環境中的重要性。負責任 AI 涉及以道德、透明、和公正的方式開發和部署 AI。核心內容包括公平性、可解釋性、隱私、穩健性、和透明度的最佳實踐。您將學習如何利用 AWS 的服務和工具，構建負責任且可靠的 AI 系統。 課程等級：基礎 授課時長：60-75 分鐘 注意：課程提供本地化註釋/字幕，旁白為英語。要.
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California Institute of Technology AWS Skill Builder

AWS Flash - Introduction to Responsible AI (Japanese)

このコースでは、責任ある AI とは何か、そしてなぜそれが生成 AI の文脈において重要なのかを概説します。責任ある AI とは、倫理的、透明、公正、かつ説明責任のある方法で AI を開発、デプロイ、使用することです。このコースでは、責任ある AI の主な要素と、公平性、説明可能性、プライバシー、堅牢性、ガバナンス、透明性に関するベストプラクティスの確立について.
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California Institute of Technology AWS Skill Builder

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

本課程概述了生成式 AI 的使用案例，及其帶來的商業價值。內容涵蓋生成式 AI 在各大產業中的實際應用和案例研究。 課程等級：基礎 持續時間：75 分鐘 注意：本課程具有本地化的註釋/字幕。旁白保留英語。要顯示字幕，請按一下播放器右下角的 CC 按鈕。 本課程內容包括簡報、實際範例和案例研究。 課程目標中，您將學習以下內容： 了解生成式 AI 的四個核心目標.
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California Institute of Technology AWS Skill Builder

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

このコースでは、生成 AI のユースケースとそのビジネス価値の概要を提供します。主要産業における実際の応用方法とケーススタディを含んでいます。コースレベルは基礎で、所要時間は75分です。 アクティビティ: このコースには、プレゼンテーション、実際の例、ケーススタディが含まれます。 コースの目標: 生成 AI の4つのコア目標を理解する 会話型 AI とパーソナラ.
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California Institute of Technology AWS Skill Builder

AWS Flash - Chalk Talks: Generative AI on AWS (Simplified Chinese)

本课程概述了可供客户在 AWS 上构建生成式人工智能应用程序的选项。 本课程将着重介绍如何利用 Amazon SageMaker Jumpstart 上的基础模型来构建生成式人工智能应用程序。 课程级别：基础级 时长：55 分钟 注意：本课程具有本地化的注释/字幕。 旁白保留英语。要显示字幕，请单击播放器右下角的 CC 按钮。 课程内容 本课程包括幻灯片内容和演示。 课程目标 在本课程中.
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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!