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Generative AI courses

1063 Courses

California Institute of Technology edX

Mastering Generative AI for Data Science

Mastering Generative AI for Data Science Generative AI is revolutionizing the data science field, and mastering its applications is vital for data science professionals. This comprehensive course offers advanced generative AI skills tailored specifically for data scientists. Dive into real-world scenarios where you will harness the power of genera.
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California Institute of Technology Southern New Hampshire University

The History and Relevance of the Rise of Generative AI

The History and Relevance of the Rise of Generative AI Dive into the fascinating journey of artificial intelligence, from its theoretical beginnings to today's powerful generative models. This course offers a unique perspective on how AI has transformed over decades, highlighting the crucial developments in deep learning that paved the way for mod.
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California Institute of Technology Southern New Hampshire University

Generative AI and Model Selection

Generative AI and Model Selection | Southern New Hampshire University | Coursera Delve into the realm of generative AI and discover the intricacies of selecting the perfect model to suit your requirements with this hands-on course. You will develop a robust understanding of generative AI models, and examine various deployment options such as web.
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California Institute of Technology Coursera

Advanced Generative Adversarial Networks (GANs)

Advanced Generative Adversarial Networks (GANs) Embark on an enlightening journey into the realm of Generative Adversarial Networks (GANs), where you will master the art of AI-driven image synthesis. This course begins with a solid foundation, introducing you to the basic concepts and components of GANs, such as the Generator and Discriminator..
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California Institute of Technology Coursera

Generative AI Content Creation

Generative AI Content Creation Dive into the innovative world of generative AI and discover how to harness its power using Adobe Firefly to elevate your creative projects. By the end of this course, you’ll be equipped with the knowledge and skills to revolutionize your design process, making you a pioneer in the digital landscape. Join us an.
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California Institute of Technology Coursera

Multimodal Generative AI: Vision, Speech, and Assistants

Multimodal Generative AI: Vision, Speech, and Assistants We are introducing a new course to replace the "Coding with ChatGPT" course in the Generative AI specialization. This updated course will cover materials, models, and content released in 2024. Some of the new additions include material on using AI for image-to-text (vision), text-to-speech,.
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California Institute of Technology Coursera

Generative AI for Executives and Business Leaders - Part 2

Generative AI for Executives and Business Leaders - Part 2 As an executive or business leader in your organization, integrating AI well is likely number one for your strategic priorities. In this short course, hear what IBM leaders think about the essentials when it comes to strategically integrating GenAI at scale cross-functi.
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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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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 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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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!