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Generative AI courses

1067 Courses

California Institute of Technology AWS Skill Builder

Planification d'un projet d'IA générative (Français) | Planning a Generative AI Project (French)

Planification d'un projet d'IA générative (Français) | Planning a Generative AI Project (French) - AWS Skill Builder Planification d'un projet d'IA générative est le deuxième cours d'une série de trois cours intitulée Notions essentielles de l'IA générative à l'intention des décideurs commerciaux et techniques. Si vous ne l'avez pas encore fait, c.
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California Institute of Technology AWS Skill Builder

Building a Generative AI-Ready Organization (Traditional Chinese)

打造生成式 AI 相關組織 (繁體中文) 「Building a Generative AI-Ready Organization」是「Generative AI Essentials for Business and Technical Decision Makers」系列的三部分課程中的最後一個課程。如果您還沒有學習此課程，建議您從該系列的第一個課程開始，名為「Introduction to Generative AI: Art of the Possible」。 完成課程後，您應該能夠說明，打造一個適合生.
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California Institute of Technology AWS Skill Builder

Building a Generative AI-Ready Organization (Simplified Chinese)

Building a Generative AI-Ready Organization (Simplified Chinese) - AWS Skill Builder “Building a Generative AI-Ready Organization” 是 “面向业务和技术决策者的生成式 AI 必修知识 (Generative AI Essentials for Business and Technical Decision Makers)” 三部分系列课程中的最后一课。如果您还未学习此系列课程中的第一课 Introduction to Generative AI: Art of t.
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California Institute of Technology AWS Skill Builder

Planning a Generative AI Project (Japanese)

Planning a Generative AI Project (Japanese) このコースでは、生成系人工知能 (AI) に関する技術的な基本と主要な用語について学びます。また、生成系 AI プロジェクトを計画するためのステップを学び、生成系 AI を使用するリスクと利点を評価します。 • コースのレベル: 初心者向け • 所要時間: 1 時間 この “Planning a Generative AI Project (Japanese)” は ”Genera.
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California Institute of Technology AWS Skill Builder

Introduction to Generative AI - Art of the Possible (Korean)

Introduction to Generative AI - Art of the Possible (Korean) '생성형 AI 소개 - 가능성의 예술' 과정에서는 생성형 AI에 대해 소개하고 사용 사례와 위험 및 이점에 대해 알아봅니다. 콘텐츠 생성 예제를 통해 가능성의 예술에 대해 설명합니다. 이 과정을 마치면 학습자는 생성형 AI의 기본 사항과 위험 및 이점에 대해 설명할 수 있습니다. 또한 콘텐츠 생성이 비즈니스.
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California Institute of Technology AWS Skill Builder

Introduction to Generative AI - Art of the Possible (Simplified Chinese)

Introduction to Generative AI - Art of the Possible (Simplified Chinese) Introduction to Generative AI - Art of the Possible 课程介绍了生成式 AI、使用案例、风险与益处。我们以内容生成为例介绍可能性的艺术。 学完本课程后，学员应当能够描述生成式 AI 的基础知识及其风险与益处。学员还应能够详述内容生成在其业务中的应用方式。.
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California Institute of Technology AWS Skill Builder

AWS で始める生成系 AI for Entry (Japanese ONLY) (Na) 日本語実写版

AWS で始める生成系 AI for Entry (Japanese ONLY) (Na) 日本語実写版 これから生成系 AI を業務で活用していく上で、そもそも生成系 AI とは何なのか、どのような技術的背景や、種類があり、業務で活用する上でのユースケースや課題を学習します。また、それらの課題に対して、AWS がどのように活用できるかを学習します。本コースは、AWS における生成系 AI の学習の第一歩.
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California Institute of Technology AWS Skill Builder

Introduction to Generative AI - Art of the Possible (Traditional Chinese)

Introduction to Generative AI - Art of the Possible (Traditional Chinese) 《Introduction to Generative AI - Art of the Possible》課程介紹了生成式 AI、使用案例、風險和優勢。藉助一個內容生成範例，我們對可能性藝術進行了說明。 本課程結束時，學員應該能夠描述生成式 AI 的基礎知識、風險和優勢。他們還應該能夠闡明如何在自己的企業中使用內容生成。 課程級別.
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California Institute of Technology AWS Skill Builder

Foundations of Prompt Engineering (Traditional Chinese)

Prompt工程基礎（繁體中文） 在本課程中，您將學到設計有效提示的原則、技巧和最佳實務。本課程將介紹提示工程設計的基礎知識，並隨著課程進展介紹進階提示技巧。您也會學習如何防範提示濫用，以及如何減輕與 FM 互動時的偏見。 課程等級：中級 持續時間：4 小時 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.
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California Institute of Technology AWS Skill Builder

Foundations of Prompt Engineering (Simplified Chinese)

Prompt Engineering Foundations (Simplified Chinese) 在本课程中，您将学习设计有效提示的原则、技术和最佳实践。本课程将介绍提示工程的基本知识，然后逐步过渡到高级提示技术。您还将学习如何防止提示误用，以及如何在与基础模型 (FM, Foundation Model) 互动时减少偏差。 课程级别：中级 时长：4 小时 注意：本课程具有本地化的注释/字幕。旁白保留英语。要.
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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!