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Generative AI courses

659 Courses

California Institute of Technology AWS Skill Builder

Essentials of Prompt Engineering (Korean)

Essentials of Prompt Engineering (Korean) 이 과정에서는 효과적인 프롬프트를 만드는 데 필요한 기본 사항을 소개합니다. 다양한 사용 사례에 맞게 프롬프트를 세분화하고 최적화하는 방법을 이해할 수 있습니다. 또한 zero-shot, few-shot 및 chain-of-thought 프롬프팅과 같은 기법도 살펴볼 수 있습니다. 마지막으로 프롬프트 엔지니어링과 관련된 잠재적 위험을 식별.
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California Institute of Technology AWS Skill Builder

Amazon Q Business Getting Started (Indonesian)

Amazon Q Business Getting Started (Indonesian) Amazon Q Business adalah asisten yang didukung kecerdasan buatan generatif (AI generatif) yang dapat menjawab pertanyaan, membuat konten, membuat ringkasan, dan menyelesaikan tugas, semua berdasarkan informasi di korporasi Anda. Dalam kursus Getting Started ini, Anda akan mempelajari manfaat, fitur,.
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Indonesian)

Amazon Q Introduction (Indonesian) Kursus ini memberikan gambaran umum tingkat tinggi tentang Amazon Q, asisten yang didukung kecerdasan buatan (AI) generatif. Anda akan mempelajari kasus penggunaan dan manfaat menghubungkan Amazon Q ke informasi, kode, dan sistem perusahaan Anda. Anda juga akan menemukan informasi tambahan untuk melanjutkan perj.
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California Institute of Technology AWS Skill Builder

Amazon Q Business Getting Started (Japanese) (Sub) 日本語字幕版

Amazon Q Business Getting Started (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。 Amazon Q Business は、生成人工知能 (生成 AI) を活用したアシスタントで、質問への回答、コンテンツの生成、要約の作成、およびタスクの完了をすべて企業内の情報に基づいて行うことができます。この入門コースでは、Amazon Q Business を使用するメリット、機.
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California Institute of Technology AWS Skill Builder

Amazon Q Business Getting Started (Japanese)

*このコースは機械翻訳で対応されています。 Amazon Q Business は、生成人工知能 (生成 AI) を活用したアシスタントで、質問への回答、コンテンツの生成、要約の作成、およびタスクの完了をすべて企業内の情報に基づいて行うことができます。この入門コースでは、Amazon Q Business を使用するメリット、機能、一般的なユースケース、技術概念、コストについて学びます。.
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Korean)

이 과정에서는 생성형 인공 지능(AI) 기반 어시스턴트인 Amazon Q에 대해 개략적으로 설명합니다. Amazon Q를 회사 정보, 코드 및 시스템에 연결할 때의 사용 사례와 이점에 대해 알아봅니다. 또한 특정 사용 사례에 대한 관심을 기반으로 학습 여정을 발전시킬 수 있는 추가 정보도 찾을 수 있습니다. 기술 학습자와 비기술 학습자 모두 어떻게 Amazon Q가 안전한 방식으로 생.
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Japanese)

Amazon Q Introduction (Japanese) *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記載されています。技術.
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California Institute of Technology AWS Skill Builder

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.
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California Institute of Technology AWS Skill Builder

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.
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California Institute of Technology AWS Skill Builder

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.
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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!