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Generative AI courses

659 Courses

California Institute of Technology AWS Skill Builder

Foundations of Prompt Engineering (Korean)

Foundations of Prompt Engineering (Korean) 이 과정에서는 효과적인 프롬프트를 설계하기 위한 원칙, 기법 및 모범 사례에 대해 알아봅니다. 이 과정에서는 프롬프트 엔지니어링의 기본 사항을 소개하고 고급 프롬프트 기법을 계속 살펴봅니다. 프롬프트 사용 오류를 방지하는 방법과 FM과의 상호 작용 시 편향을 완화하는 방법을 알아봅니다. 과정 수준: 중급 소.
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California Institute of Technology AWS Skill Builder

Generative AI for Executives (Japanese) (VO) 日本語吹き替え版

Generative AI for Executives (Japanese) (VO) 日本語吹き替え版 このコースでは、生成系 AI の概要を説明します。受講者は、生成系 AI とは何か、それがどのようにして経営者の懸念や課題に対応するのか、またどのようにしてビジネスの成長をサポートするのかを学びます。また、AI が数多くの業界に大変革をもたらす可能性をどれほど秘めているのかも学びます。 ＊このコ.
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California Institute of Technology AWS Skill Builder

Introduction to Generative AI - Art of the Possible (Japanese) (Sub) 日本語字幕版

Introduction to Generative AI - Art of the Possible (Japanese) (Sub) 日本語字幕版 Introduction to Generative AI - Art of the Possible (Japanese) コースでは、生成系 AI とそのユースケース、リスクと利点について紹介します。コンテンツ生成の事例を通じて、可能性の技術を示します。このコースを修了すると、受講者は生成系 AI、およびそのリスクと利点の基本を説.
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California Institute of Technology AWS Skill Builder

Pianificare un progetto di IA generativa (Italiano) | Planning a Generative AI Project (Italian)

Pianificare un progetto di IA generativa (Italiano) | Planning a Generative AI Project (Italian) Pianificare un progetto di IA generativa è il secondo corso della serie in tre parti denominata: Elementi essenziali di IA generativa per i responsabili delle decisioni tecniche e aziendali. Se non lo hai già fatto, inizia dal primo corso della serie:.
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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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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!