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Generative AI courses

664 Courses

California Institute of Technology Pluralsight

Image Generation with DALL-E 3

Image Generation with DALL-E 3 Image-based generative AI allows the creation of stunning and useful images via prompts. This course will teach you how to generate high-quality images using the power of Generative AI with DALL-E. In this course, Image Generation with DALL-E, you’ll learn how to harness the power of generative AI for image creati.
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California Institute of Technology Coursera

Creating Social Media Posts with GenAI: Mastering ChatGPT

Creating Social Media Posts with GenAI: Mastering ChatGPT Are you ready to elevate your social media game using the latest in generative AI? In today’s fast-paced digital world, creating engaging content that resonates with your audience is key to standing out. This Guided Project is designed to help social media managers, marketers, and cont.
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California Institute of Technology AWS Skill Builder

AWS Flash - Automating with AI/ML for Small Business Owners

AWS Flash - Automating with AI/ML for Small Business Owners This course provides small business owners with a high-level overview of generative artificial intelligence (AI). Learners will explore the basics of generative AI technology and how to implement it to help their business expand into new markets, increase customer satisfaction, and enable.
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California Institute of Technology Coursera

Gemini in Google Drive - 简体中文

标题: Gemini in Google Drive - 简体中文 描述: Gemini for Google Workspace 是一个插件，用户可通过它来使用生成式 AI 功能。本课程通过视频课程、实操活动和实际示例，深入探讨了“Google 云端硬盘中的 Gemini”的功能。 学完本课程后，您将掌握相关知识和技能，能够自信地利用 Google 云端硬盘中的 Gemini 来改进工作流。 大学: 提供者: Coursera 分类: Cloud Computing.
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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!