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Generative AI courses

659 Courses

California Institute of Technology University of Michigan

Generative AI: Forecasting Disruption

For centuries, revolutionary technologies like the printing press and computers have been considered major disruptors of both work and life. "Generative AI: Forecasting Disruption" is designed to help you understand our reactions to the rise of generative AI by revisiting these technological milestones. Discover the evolution of commun.
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California Institute of Technology XuetangX

ChatGPT与AIGC技术的前沿探索

探索ChatGPT和AIGC技术的前沿，通过现场直播课程获取深刻的见解。这些课程涵盖了人工智能和内容生成的最新发展、实际应用以及未来的发展趋势，为您揭示这些令人兴奋的技术的广阔可能性。由XuetangX提供的尖端课程适合对AI和生成内容充满热情的学习者。
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California Institute of Technology XuetangX

系列综述带你了解ChatGPT、AIGC和扩散模型

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California Institute of Technology Pluralsight

Introduction to Amazon Q Business

Generative AI is revolutionizing industries, but businesses require more tailored solutions, specifically utilizing AI on their data. This course, "Introduction to Amazon Q Business," equips you with the skills to establish a customized Amazon Q Business Assistant designed to interact with enterprise data sources. In this comprehensive cou.
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California Institute of Technology XuetangX

Principles and Practice of Artificial Intelligence

In July 2017, China's State Council unveiled the "New Generation Artificial Intelligence Development Plan," a pivotal move that positioned AI advancement as a national strategic focus. As AI drives high-quality development, it reshapes the concept of new productive forces and stands as a measure of a nation's technological innovation and prowess in.
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California Institute of Technology Udemy

ChatGPT Masterclass: ChatGPT Guide for Beginners to Experts!

Generative AI Courses ChatGPT Courses Search Engine Optimization (SEO) Courses E-commerce Courses Content Creation Courses Join our ChatGPT Masterclass on Udemy to transform your understanding from beginner to expert. This course focuses on the powerful ChatGPT 4 and includes lessons on prompt engineering and leveraging generative A.
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California Institute of Technology Udemy

ChatGPT, Midjourney, Firefly, Bard, DALL-E, AI Crash Course

Unlock the power of artificial intelligence with our intensive AI Crash Course. In just 15 minutes, you'll gain essential skills to start utilizing the most cutting-edge AI tools including ChatGPT, Midjourney, Google Bard, Adobe Firefly, and DALL-E. This course, provided by Udemy, is a top choice among Generative AI Courses and offers specializ.
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California Institute of Technology Udemy

2024 Bootcamp: Generative AI + LLM App Development.

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California Institute of Technology Udemy

Ultimate AI Art Content Creation Course (Generative AI)

Unlock Your Creative Potential with AI Embark on a revolutionary journey in the realm of digital creativity with our Ultimate AI Art Content Creation Course, available exclusively on Udemy. This expansive program delves into the capabilities of generative AI, encompassing the creation of images, videos, audio, and textual content..
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California Institute of Technology Udemy

ChatGPT, Midjourney, DALL-E 3 & APIs - The Complete Guide

Unlock the secrets of maximizing your productivity with our complete guide on ChatGPT, Midjourney, DALL-E 3, and APIs. Designed to elevate your skills, this course offers a deep dive into the world of AI with practical applications in various APIs. Gain expertise in prompt engineering, navigate the unique features of the Cursor IDE, and harness.
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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!