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Generative AI courses

1062 Courses

California Institute of Technology Canvas Network

GenAI in Action: Impact and Possibilities

Explore the boundless potential of Artificial Intelligence with the comprehensive AI and ChatGPT course from the prestigious University of South Florida. This expertly designed course, offered through Canvas Network, delves deep into the realms of AI, machine learning, and generative AI. Whether you're interested in enhancing your knowledge of.
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California Institute of Technology AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (ไทย)

ในหลักสูตรนี้ คุณจะสำรวจวงจรชีวิตของแอปพลิเคชันปัญญาประดิษฐ์ช่วยสร้าง (Generative AI หรือ AI ช่วยสร้าง) ได้แก่ การกำหนดกรณีใช้งานทางธุรกิจ การเลือกโมเดลพื้นฐาน (FM) การปรับปรุงประสิทธิภาพของ FM การประเมินประสิทธิภาพของ FM การนำไปใช้จริงและผลกระทบต่อวัตถุประสงค์ทางธุรกิจ หลักสูตรนี้เป็นการปูพื้นฐานไปสู่หลักสูตร AI ช่วยสร้างหลักสูตรต.
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California Institute of Technology AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (Bahasa Indonesia)

Dalam kursus ini, Anda akan belajar tentang dasar-dasar machine learning (ML) dan kecerdasan buatan (AI). Anda akan melihat berbagai bentuk hubungan antara AI, ML, deep learning, dan bidang kecerdasan buatan generatif yang sedang berkembang (AI generatif). Anda akan mendapatkan pemahaman yang sangat baik tentang istilah AI dasar, sehingga me.
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California Institute of Technology AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (한국어)

이 과정에서는 다음을 포함하는 생성형 인공 지능(생성형 AI) 애플리케이션 수명 주기를 살펴봅니다. 비즈니스 사용 사례 정의 파운데이션 모델(FM) 선택 FM 성능 개선 FM 성능 평가 배포 및 비즈니스 목표에 미치는 영향 이 과정은 생성형 AI 입문 과정으로, 프롬프트 엔지니어링, 검색 증강 생성(RAG), 파인 튜닝을 사용한 FM 사용자 지정과 관련된 개념을.
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California Institute of Technology AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (한국어)

이 과정에서는 다양한 산업의 실제 인공 지능, 기계 학습(ML) 및 생성형 인공 지능(생성형 AI) 사용 사례를 살펴봅니다. 이러한 산업에는 의료, 금융, 마케팅, 엔터테인먼트 등이 포함됩니다. 또한 AI, ML, 생성형 AI의 기능 및 제한 사항, 모델 선택 기법, 주요 비즈니스 지표에 대해서도 알아봅니다. 과정 수준: 기초 소요 시간: 1시간 이 과정에는 대화형 요소,.
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California Institute of Technology AWS Skill Builder

Responsible Artificial Intelligence Practices (한국어)

In this course, you will explore responsible AI practices. It begins with an introduction to what responsible AI means, including defining responsible AI, understanding the challenges it seeks to overcome, and exploring its core elements. Next, the course delves into various topics for developing responsible AI systems and introduces the services.
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California Institute of Technology AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (简体中文)

在本课程中，您将探索生成式人工智能应用程序生命周期，其中包括以下内容： 定义业务使用案例 选择基础模型 (FM) 提高 FM 的性能 评估 FM 的性能 部署及其对业务目标的影响 本课程是生成式人工智能课程的入门课程，这些课程深入探讨了使用提示工程、检索增强生成 (RAG) 和微调技术自定义 FM 的相关概念。 课程级别：基础级 时长：1 小时 注意：本课程具有.
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California Institute of Technology AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (简体中文)

在本课程中，您将探索现实世界各行各业中人工智能 (AI)、机器学习 (ML) 和生成式人工智能的使用案例。这些领域包括医护、金融、营销、娱乐等。您还将了解 AI、ML 和生成式人工智能的能力和局限性、模型选择技巧和关键业务指标。 课程级别：基础级 时长：1 小时 注意：本课程具有本地化的注释/字幕。 旁白保留英语。要显示字幕，请单击播放器右下角的 CC 按钮。 课.
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California Institute of Technology AWS Skill Builder

Responsible Artificial Intelligence Practices (简体中文)

在本课程中，您将学习负责任的 AI 实践。首先，您将了解什么是负责任的 AI，学习定义与挑战，并探索其核心维度。 然后，深入开发负责任的 AI 系统，了解 AWS 提供的服务和工具，以及在模型选择和数据准备中的注意事项。 最后，您将了解透明且可解释的模型，探索其权衡考虑因素及以人为本的设计原则。 课程级别：基础级 时长：1 小时 课程内容：包含互动元素、文字.
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California Institute of Technology Udemy

Generative AI: Learn about the next AI frontier

Join the frontier of artificial intelligence with our comprehensive course on Generative AI. Understand the advantages, drawbacks, and unforeseen consequences that accompany this technology. Gain insights through specialized courses covering: Generative AI Courses Machine Learning Courses ChatGPT Courses DALL-E Courses Stable Dif.
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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!