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Generative AI courses

659 Courses

California Institute of Technology FGV Educação Executiva

IA Generativa e o ChatGPT: Potencializando o Trabalho e Ganhando Eficiência

Explore como o uso da Inteligência Artificial pode transformar o ambiente educacional e profissional. O curso IA Generativa e o ChatGPT: Potencializando o Trabalho e Ganhando Eficiência oferecido pela FGV Educação Executiva destina-se a ensinar o uso eficaz de ferramentas de IA, desde a análise de dados educacionais até a criação de experiênc.
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California Institute of Technology Coursera

Üretken Yapay Zekaya Giriş

Üretken yapay zekaya giriş yaparak bu heyecan verici ve hızla gelişen alan hakkında temel bilgileri edinin. Kurs, üretken yapay zekanın ne olduğunu, nasıl işlediğini ve geleneksel makine öğrenim yöntemlerinden nasıl farklı olduğunu açıklamayı hedefliyor. Öğrenme sürecinizde size rehberlik etmek için Google'ın güçlü araçlarını tanıyacak ve uy.
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California Institute of Technology Microsoft Learn

Evaluate generative AI applications

Explore our specialized course designed to equip you with the skills to evaluate generative AI applications effectively. This course is divided into two essential modules: Module 1: Fundamental Concepts Grasp the core principles needed for evaluating generative AI applications. By completing this module, you will be adept at:.
California Institute of Technology SAP Learning

AI Ethics at SAP

SAP recognizes the transformative potential of artificial intelligence (AI) for businesses, governments, and society. However, the rapid adoption of AI technologies brings with it various economic, political, and social challenges. The current pace of technological advancement has outstripped the establishment of sufficient governmental guidelin.
California Institute of Technology Coursera

Build an application to send Chat Prompts using the Gemini model

Build an application to send Chat Prompts using the Gemini model This self-paced lab takes place in the Google Cloud console. In this lab, you will learn how to use Google's Vertex AI SDK to interact with the powerful Gemini generative AI model, enabling you to send text-based chat prompts as an input and receive personalized streaming and non-.
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California Institute of Technology AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (Indonesian)

Developing Generative Artificial Intelligence Solutions (Indonesian) Dalam kursus ini, Anda akan menjelajahi siklus hidup aplikasi kecerdasan buatan generatif (AI generatif), yang meliputi hal-hal berikut: Mendefinisikan kasus penggunaan bisnis Memilih model fondasi (FM) Meningkatkan kinerja FM Mengevaluasi kinerja FM Penggunaan dan da.
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California Institute of Technology AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (Traditional Chinese)

探索人工智慧的使用案例與應用（繁體中文） 在本課程中，您將探索各種行業中人工智慧 (AI)、機器學習 (ML) 和生成式人工智慧 (生成式 AI) 的實際使用案例。這些領域包括醫療保健、金融、行銷、娛樂等。您還將了解 AI、ML 和生成式 AI 功能和限制、模型選取技術，以及關鍵業務指標。 課程等級：基礎 課程時長：1 小時 注意：本課程具有本地化的註釋/字幕。旁白保留英語。.
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California Institute of Technology Coursera

Getting started with the Vertex AI Gemini 1.5 Pro Model

Getting Started with the Vertex AI Gemini 1.5 Pro Model Getting Started with the Vertex AI Gemini 1.5 Pro Model Immerse yourself in a self-paced lab within the Google Cloud console, designed to offer an introductory and practical experience with Generative AI on Google Cloud. This engaging lab is provided by Coursera. Categories: A.
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California Institute of Technology AWS Skill Builder

Responsible Artificial Intelligence Practices (Traditional Chinese)

負責任的人工智慧實踐 (繁體中文) 在本課程中，您將學習 AI 實踐。首先，將為您介紹什麼是負責任的 AI。您將學習如何定義負責任的 AI、瞭解負責任的 AI 嘗試克服之挑戰，以及探索負責任 AI 的核心維度。 然後，您將深入探索一些主題，瞭解開發負責任的 AI 系統。將為您介紹由 AWS 提供，以協助您使用負責任的 AI 之服務和工具。您也將瞭解在為 AI 系統選擇模型和準備資料時，.
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California Institute of Technology AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (Traditional Chinese)

開發生成式人工智慧解決方案 (繁體中文) 在本課程中，您將會探索生成式人工智慧 (生成式 AI) 應用程式生命週期，其中包括以下內容： 定義商業使用案例 選取基礎模型 (FM) 改善 FM 的效能 評估 FM 的效能 部署及其對業務目標的影響 本課程是生成式 AI 課程的入門，其中深入探討使用提示詞工程、檢索增強生成 (RAG) 和微調來自訂 FM 的相關概念。 課程等級：基礎.
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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!