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Generative AI courses

1063 Courses

California Institute of Technology AWS Skill Builder

AWS ML Engineer Associate Curriculum Overview (Korean)

AWS ML 엔지니어 어소시에이트 교육 과정의 이 입문자용 과정에서는 기계 학습(ML) 관련 기본 사항을 복습하고 ML 및 AI의 발전 상황을 살펴봅니다. 비즈니스 목표를 파악하고 ML 문제를 공식화하며 Amazon SageMaker에 대해 알아봅니다. 과정 수준: 고급 소요 시간: 45분 참고: 이 과정의 동영상에는 한국어 트랜스크립트 또는 자막이 지원됩니다. 자막을 표시하.
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California Institute of Technology AWS Skill Builder

Optimizing Foundation Models (Traditional Chinese)

在本課程中，您將探索兩種改善基礎模型 (FM) 效能的技術：檢索增強生成 (RAG) 和微調。您將了解 Amazon Web Services (AWS) 服務，這些服務有助於使用向量資料庫儲存內嵌、客服在多步驟任務中的角色、定義微調 FM 的方法、如何準備用於微調的資料等等。 課程等級：基礎 課程時長：1 小時 本課程包括互動元素、文字指令和說明性圖形。 課程目標 識別有助於使用向量.
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Simplified Chinese)

本课程概述了 Amazon Q，这是一款基于生成式人工智能 (AI) 的助手。您将了解将 Amazon Q 与贵公司信息、代码和系统相关联的使用案例和益处。您还将根据您对特定使用案例的兴趣查找其他信息，以推进您的学习之旅。技术型和非技术型学员都将了解 Amazon Q 如何以安全可靠的方式提高其生产力。 课程级别：基础级 时长：15 分钟 注意：本课程具有本地化的注释/字幕。 旁白保留.
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California Institute of Technology AWS Skill Builder

Amazon Q Introduction (Traditional Chinese)

本課程提供 Amazon Q 的高階概觀，這是生成式人工智慧 (AI) 支援助理。您將會了解將 Amazon Q 連結至公司資訊、程式碼和系統的使用案例和優點。您也可以找到其他資訊，以根據您對特定使用案例的興趣推進學習旅程。技術和非技術學習者都會了解 Amazon Q 如何以安全可靠的方式提高他們的生產力。 課程等級：基礎 持續時間：15 分鐘 注意：本課程具有本地化的註釋/字幕。.
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California Institute of Technology AWS Skill Builder

BI generativa com o Amazon Q no Quicksight: introdução (Português) | Generative BI with Amazon Q in Quicksight - Getting Started (Portuguese)

O Amazon Q no QuickSight traz um conjunto inovador de recursos de business intelligence (BI) ao integrar grandes modelos de linguagem (LLMs) do Amazon Bedrock com as capacidades do Amazon QuickSight. Durante este curso, você explorará conceitos técnicos e entenderá os benefícios de utilizar o Amazon Q no QuickSight. Descubra a arquitetura e os recu.
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California Institute of Technology AWS Skill Builder

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

Amazon Q in QuickSight integrates Amazon Bedrock's large language models (LLM) with Amazon QuickSight's AI features, introducing new Business Intelligence (BI) capabilities. In this course, you will learn the technical concepts and advantages of using Amazon Q in QuickSight. You will discover the architecture of Amazon Q in QuickSight and how.
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California Institute of Technology Southern New Hampshire University

From Data to Decisions: Getting Started with AI

Embark on your AI journey and transform your organizational data into actionable insights with Southern New Hampshire University's course, "From Data to Decisions: Getting Started with AI," available on Coursera. This course caters to individuals eager to work with organizational data but unsure where to begin. Leverage the power of generative A.
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California Institute of Technology SAP Learning

Generative AI at SAP

Discover how artificial intelligence (AI) transforms business processes with the 'Generative AI at SAP' course. This program provides a comprehensive understanding of AI's fundamental uses and benefits in a professional setting. Participants will explore different AI methodologies and study detailed use cases. Upon completion, attendees will gain.
California Institute of Technology Coursera

Interactive and Immersive Experiences with Generative AI

Embark on a transformative journey with the "Interactive and Immersive Experiences with Generative AI" course, your gateway to leveraging AI in the realms of creative industries and interactive media. Delve into a curriculum that seamlessly blends theoretical insights with practical demonstrations to empower your creative collaborations and re.
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California Institute of Technology AWS Skill Builder

Princípios da engenharia de prompts (Português) | Foundations of Prompt Engineering (Portuguese)

Neste curso, você aprenderá os princípios, as técnicas e as práticas recomendadas para criar prompts eficazes. Este curso apresenta os elementos básicos da engenharia de prompts e avança para técnicas avançadas de prompts. Você também aprenderá a se proteger contra o uso indevido de prompts e a mitigar a interação com FMs. Nível do curso: int.
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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!