Generative AI courses

542 Courses

Présentation de l'IA générative – L'Art du possible (Français) | Introduction to Generative AI - Art of the Possible (French)

Présentation de l'IA générative – L'Art du possible (Français) | Introduction to Generative AI - Art of the Possible (French) Le cours Présentation de l'IA générative – L'Art du possible propose une présentation de l'IA générative, ses cas d'utilisation, ses risques et ses avantages. À l'aide d'un exemple de génération de contenu, nous illustrons.
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Building a Generative AI-Ready Organization (Thai)

Building a Generative AI-Ready Organization (Thai) Building a Generative AI-Ready Organization เป็นหลักสูตรสุดท้ายในเนื้อหาชุดแบบสามตอน ที่เรียกว่า Generative AI Essentials สำหรับผู้มีอำนาจตัดสินใจทางธุรกิจและทางเทคนิค หากคุณยังไม่ได้ศึกษาหลักสูตร เราขอแนะนำให้คุณเริ่มด้วยหลักสูตรแรกในชุดที่มีชื่อว่า Introduction to Generative AI: Art of the Poss.
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Desarrollo de una organización preparada para la IA generativa (Español LATAM) | Building a Generative AI-Ready Organization (LATAM Spanish)

Desarrollo de una organización preparada para la IA generativa (Español LATAM) | Building a Generative AI-Ready Organization (LATAM Spanish) Desarrollo de una organización preparada para la IA generativa es el último curso en la serie de tres partes de Fundamentos de la IA generativa para los responsables de decisiones técnicas y comerciales. Si a.
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AWS で始める生成 AI スキルアップシリーズ (Japanese ONLY) (Na) 日本語実写版

AWS で始める生成 AI スキルアップシリーズ (Japanese ONLY) (Na) 日本語実写版 このコースは、生成 AI 活用の基礎から実践までを段階的に学べるよう、3つのトレーニングで構成されています。 最初のトレーニング「AWS で始める生成系 AI for Entry」では、そもそも生成 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.
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IA générative pour les cadres (Français) | Generative AI for Executives (French)

IA générative pour les cadres (Français) | Generative AI for Executives (French) Ce cours fournit une vue d'ensemble de l'IA générative. Les étudiants découvrent ce qu'est l'IA générative, comment elle peut répondre aux préoccupations et défis des cadres et comment elle peut soutenir la croissance commerciale. Ils apprennent également comment l'I.
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Generative AI for Executives (Japanese) (Sub) 日本語字幕版

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。 *このコースの中のビデオには日本語の字幕がついています。字幕を表示させ.
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Generative AI for Executives (Korean) (Na) (한국어 강의)

Generative AI for Executives (Korean) (Na) (한국어 강의) 이 과정에서는 Generative AI에 대해 간략히 설명합니다. 학습자는 Generative AI가 무엇이고, 경영진의 우려 사항과 과제를 어떻게 해결할 수 있으며, 비즈니스 성장을 어떻게 지원하는지 살펴봅니다. 또한 Generative AI가 수많은 산업을 혁신하는 방식에 대해서도 알아봅니다. 과정 수준: 기초 소요 시간:.
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Generative AI for Executives (Thai)

Generative AI for Executives (Thai) หลักสูตรนี้ให้ภาพโดยรวมของ Generative AI ผู้เรียนจะได้สำรวจว่า Generative AI คืออะไร สามารถจัดการกับข้อกังวลและความท้าทายของผู้บริหารได้อย่างไร และจะสนับสนุนการเติบโตของธุรกิจได้อย่างไร นอกจากนี้ ผู้เรียนยังจะได้เรียนรู้ว่า Generative AI มีศักยภาพในการปฏิวัติอุตสาหกรรมมากมายได้อย่างไรอีกด้วย ระดับหลักสูตร: พ.
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Planning a Generative AI Project (Traditional Chinese)

Planning a Generative AI Project (Traditional Chinese) Planning a Generative AI Project是名為「Generative AI Essentials for Business and Technical Decision Makers」(業務和技術決策者的生成式 AI 基礎知識) 系列的三部分課程中的第二門課程。如果您還沒有學習此課程,請從該系列的第一門課程開始,名為Introduction to Generative AI - Art of the Poss.
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Planning a Generative AI Project (Simplified Chinese)

Planning a Generative AI Project (Simplified Chinese) Planning a Generative AI Project 是 Generative AI Essentials for Business and Technical Decision Makers 三部分系列课程中的第二课。如果您还未学习此系列课程中的第一课 Introduction to Generative AI - Art of the Possible,请先学习第一课。 在本课程中,您将学习与生成式人工智能 (AI) 相关的技术基础和.
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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!