Generative AI courses

1093 Courses

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Generative BI with Amazon Q in Quicksight - Getting Started (Japanese)

*このコースは機械翻訳で対応されています。 Amazon Q in QuickSight では、Amazon Bedrock の大規模言語モデル (LLM) を使用し、それらを Amazon QuickSight の機能と組み合わせることにより、新しいビジネスインテリジェンス (BI) 機能スイートが導入されています。このコースでは、QuickSight で Amazon Q を使用する場合の技術的概念と利点について学びます。Amazon Q.
provider AWS Skill Builder
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Amazon Q Business Getting Started (Simplified Chinese)

Amazon Q Business Getting Started (Simplified Chinese) Amazon Q Business 是一款生成式人工智能(生成式 AI)驱动的助理,可以完全根据企业内部的信息回答问题、生成内容、创建摘要和完成任务。在本入门课程中,您将了解使用 Amazon Q Business 的优势、功能、典型使用案例、技术概念和成本。您还将查看描述 Amazon Q Business 工作原理的架构。通过由解说视频、分步说明.
provider AWS Skill Builder
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Amazon Q Introduction (Japanese) (Sub) 日本語字幕版

Amazon Q Introduction (Japanese) (Sub) 日本語字幕版 *このコースは機械翻訳で対応されています。 このコースでは、生成人工知能 (AI) 搭載アシスタントである Amazon Q の概要を説明します。Amazon Q を会社の情報、コード、システムにリンクすることのユースケースと利点について学びます。また、特定のユースケースへの関心に基づいて、学習を進めるための追加情報も.
provider AWS Skill Builder
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Generative BI with Amazon Q in Quicksight - Getting Started (Japanese) 日本語字幕版

*このコースは機械翻訳で対応されています。 Amazon Q in QuickSight では、Amazon Bedrock の大規模言語モデル (LLM) を使用し、それらを Amazon QuickSight の機能と組み合わせることにより、新しいビジネスインテリジェンス (BI) 機能スイートが導入されています。このコースでは、QuickSight で Amazon Q を使用する場合の技術的概念と利点について学びます。Amazon Q i.
provider AWS Skill Builder
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AWS Flash - A Hands-On Look at Amazon Q Business Expert

AWS Flash - A Hands-On Look at Amazon Q Business Expert This course has been created with the help of an Amazon Q business expert. Amazon Q business expert assists businesses in empowering various users to gain insights from company data, leading to better decision-making. It can be tailored to a business by connecting it to the company's data, inf.
provider AWS Skill Builder
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AWS Flash - Hands-On Demonstration of Amazon Q for Business Use

AWS Flash - Hands-On Demonstration of Amazon Q for Business Use | AWS Skill Builder AWS Flash - Hands-On Demonstration of Amazon Q for Business Use Join us for an in-depth, hands-on demonstration of Amazon Q, designed specifically for business use. Enhance your skills in various cutting-edge fields including busine.
provider AWS Skill Builder
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What Is Generative AI?

What Is Generative AI? Discover the world of Generative AI with LinkedIn Learning’s comprehensive course. Delve into the fundamentals of generative AI, from its historical context and popular models to the intricate mechanics and ethical considerations of this transformative technology.
provider LinkedIn Learning
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AI for business - AI 101 fundamentals for managers & leaders

AI for Business - AI 101 Fundamentals for Managers & Leaders | Udemy Course Title: AI for Business - AI 101 Fundamentals for Managers & Leaders Description: Gain a solid understanding of Artificial Intelligence and how it can be applied in business contexts. This comprehensive course will help you explore all opportunities and manage potential ri.
provider Udemy
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Generative AI for Business Leaders: A Quick Overview

Generative AI for Business Leaders: A Quick Overview - Udemy Course Title: Generative AI for Business Leaders: A Quick Overview Description: Learn how to use AI with your team. This brief introduction covers simple frameworks and strategies for using Gen AI at work. University: University Provider: Udemy Categories: Artificial Intelligence Cour.
provider Udemy
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GenAI for Mobile App Developers (iOS, Android)

GenAI for Mobile App Developers (iOS, Android) At the GenAI Academy, the "GenAI for Mobile App Developers" course dives into the transformative potential of Generative Artificial Intelligence (GenAI) in mobile app development. This comprehensive course empowers participants to harness GenAI capabilities, streamlining their development workflows..
provider Coursera

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!