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Generative AI courses

542 Courses

California Institute of Technology Pluralsight

OpenAI: Prompt Engineering for IT Administrators

OpenAI: Prompt Engineering for IT Administrators Course Title: OpenAI: Prompt Engineering for IT Administrators Provided by: Pluralsight Description: IT operations professionals can benefit greatly from generative AI in general and OpenAI ChatGPT in particular. This course teaches you how to craft ChatGPT prompts for maximum accuracy and efficie.
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California Institute of Technology Pluralsight

The Data Sessions: Prompt Engineering for Marketing Professionals

The Data Sessions: Prompt Engineering for Marketing Professionals Unlock the potential of cutting-edge AI techniques to elevate your marketing strategies. In a world where personalized marketing is crucial, marketing professionals face the challenge of creating tailored content at scale. This course equips learners with the skills to leverage gen.
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California Institute of Technology Udemy

【ChatGPT】使い方入門-生成AIをビジネス活用！初心者向け講座【Copilot,画像生成】2024年最新版

【ChatGPT】使い方入門-生成AIをビジネス活用！初心者向け講座【Copilot,画像生成】2024年最新版 Provider: Udemy Generative AI Courses, ChatGPT Courses, Image Generation Courses ChatGPTやCopilot、Midjourneyなど、幅広い生成AIツールをビジネスの現場で使いこなし、最先端のAI仕事術を実現しましょう！11/1に公開されたMicrosoft 365 Copilotの活用術も疑似体験でき.
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California Institute of Technology Coursera

Gemini for Cloud Architects - 简体中文

Gemini for Cloud Architects - 简体中文 在本课程中，您将了解 Gemini（Google Cloud 的生成式 AI 赋能的协作工具）如何帮助管理员预配基础设施。您将了解如何通过输入提示来让 Gemini 解释基础设施、GKE 集群的部署，以及现有基础设施的更新。您可以通过实操实验了解如何利用 Gemini 来改进 GKE 部署工作流。Duet AI 已更名为 Gemini，这是我们的新一代模型。 提供者：.
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California Institute of Technology Coursera

Gemini for Cloud Architects - 繁體中文

Gemini for Cloud Architects - 繁體中文 | Coursera 本課程介紹的 Gemini 是採用生成式 AI 技術的協作工具，可協助管理員在 Google Cloud 佈建基礎架構。您將瞭解如何透過提示讓 Gemini 解釋基礎架構、部署 GKE 叢集，以及更新既有的基礎架構。在實作研究室中，您也會體驗到 Gemini 如何改良 GKE 的部署工作流程。 Duet AI 已更名為 Gemini，這是我們的新一代.
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California Institute of Technology Coursera

Gemini for Security Engineers - Português Brasileiro

Gemini for Security Engineers - Português Brasileiro Neste curso, você vai entender como o Gemini, um colaborador com tecnologia de IA generativa do Google Cloud, ajuda a proteger seu ambiente e recursos de nuvem. Você vai aprender a implantar exemplos de cargas de trabalho em um ambiente no Google Cloud, identificar e corrigir configurações in.
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California Institute of Technology Coursera

Gemini for Application Developers - 한국어

Gemini for Application Developers - 한국어 이 과정에서는 Google Cloud의 생성형 AI 기반 공동작업 도구인 Gemini가 개발자의 애플리케이션 빌드에 어떤 도움이 되는지 알아봅니다. Gemini에 프롬프트를 입력하여 코드에 대한 설명을 얻고 Google Cloud 서비스를 추천받고 애플리케이션의 코드를 생성하는 방법을 배울 수 있습니다. 실무형 실습을 통해 Gemini로 애플리.
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California Institute of Technology Coursera

Gemini for Cloud Architects - Deutsch

Gemini für Cloud-Architekten - Deutsch | Coursera In diesem Kurs erfahren Sie, wie Gemini, ein auf generativer KI basierendes Produkt von Google Cloud, Administratoren bei der Bereitstellung von Infrastruktur unterstützt. Sie lernen die Prompts kennen, mit denen Gemini Infrastruktur erklären, GKE-Cluster bereitstell.
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California Institute of Technology Coursera

Gemini for Cloud Architects - 日本語版

Gemini for Cloud Architects - 日本語版 University:   Provider:   Coursera Categories:   Generative AI Courses, Google Cloud Platform (GCP) Courses, Cloud Architecture Courses, Infrastructure Provisioning Courses このコースでは、Google Cloud の生成 AI を活用したコラボレーターである Gemini が、管理者によるインフラストラクチャ.
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California Institute of Technology Coursera

Introduction to Large Language Models - 日本語版

Introduction to Large Language Models - 日本語版 このマイクロラーニングコースでは、大規模言語モデル（LLM）とは何か、どのようなユースケースで活用できるのか、プロンプト調整でLLMのパフォーマンスを高めるにはどうすればよいかについて学習します。ジェネレーティブAIアプリを自分で作成するのに役立つGoogleツールについても紹介します。 提供元: Coursera カテゴ.
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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!