Join

Generative AI courses

658 Courses

California Institute of Technology Coursera

Advanced AI and Machine Learning Techniques and Capstone

Join our Advanced AI and Machine Learning Techniques and Capstone course to explore high-level AI & ML strategies. This course culminates in a capstone project where you'll utilize comprehensive skills to tackle a real-world challenge. Throughout, you'll encounter state-of-the-art machine learning methods and delve into the ethical consider.
course image
California Institute of Technology Coursera

AI and Machine Learning Algorithms and Techniques

Delve into the essential algorithms and methodologies at the heart of AI and Machine Learning with this in-depth course. Discover how pre-trained large-language models (LLMs) and various learning paradigms like supervised, unsupervised, and reinforcement learning come together to solve complex business challenges. Gain hands-on experience in.
course image
California Institute of Technology University of Michigan

AI Ethics, Responsible Use, and Creativity

AI Ethics, Responsible Use, and Creativity Course - University of Michigan Join the University of Michigan's course on "AI Ethics, Responsible Use, and Creativity," offered through Coursera. This course delves into the ethics and responsible use of generative AI tools, specifically for creative work. By the end of this course, participants will.
course image
California Institute of Technology University of Michigan

AI Basics and Tools for Creativity

Embark on a journey of creativity with the course "AI Basics and Tools for Creativity," designed to integrate artificial intelligence into your artistic endeavors. This course offered by the University of Michigan through Coursera provides comprehensive insights into the use of AI in both music and design, alongside a general overview of the.
course image
California Institute of Technology Coursera

Microsoft AI & ML Engineering

Join the Microsoft AI & ML Engineering program, a detailed curriculum designed to equip you with essential skills and deep understanding in the flourishing fields of Artificial Intelligence and Machine Learning. Across five courses, this program covers foundational concepts to advanced topics, offering practical skills and hands-on experience i.
course image
California Institute of Technology University of Michigan

AI for Creative Work

Unlock the potential of generative artificial intelligence within creative domains through the University of Michigan's comprehensive Coursera specialization. This course offers an in-depth exploration of AI tools, delves into ethical and responsible AI use, and provides a holistic view of AI's influence on human artistic expression. Partici.
course image
California Institute of Technology University of Michigan

Navigating Disruption: Generative AI in the Workplace

Generative AI is set to become one of the most transformative technologies of our time. Join the course series "Navigating Disruption: Generative AI in the Workplace" to gain a clear understanding of how this technology functions, learn from historical tech disruptions, and discover the potential roles AI might assume in future workplaces. O.
course image
California Institute of Technology AWS Skill Builder

AWS Flash - Chalk Talks: Generative AI on AWS (Korean)

이 과정에서는 고객이 AWS에서 생성형 AI 애플리케이션을 빌드할 때 사용할 수 있는 옵션을 간략하게 설명합니다. 이 과정은 특히 Amazon SageMaker JumpStart의 파운데이션 모델을 활용하여 생성형 AI 애플리케이션 빌드를 중점적으로 설명합니다. 과정 수준: 기초 소요 시간: 55분 참고: 이 과정의 동영상에는 한국어 트랜스크립트 또는 자막이 지원되며 음성은.
course image
California Institute of Technology AWS Skill Builder

AWS Flash - Chalk Talks: Generative AI on AWS (Japanese)

このコースでは、AWS で生成 AI アプリケーションを構築しようとしているお客様が利用できるさまざまなオプションについて解説します。このコースでは特に、Amazon SageMaker Jumpstart の基盤モデルを活用して生成 AI アプリケーションを構築する方法に焦点を当てます。 コースレベル: 基礎 所要時間: 55 分 このコースにはスライドとデモが含まれています。 このコ.
course image
California Institute of Technology Microsoft Learn

Ethical AI

Ethical AI Enhance your understanding of ethical AI with Microsoft's comprehensive course. This program covers critical areas such as AI workloads and Azure AI Services, emphasizing Microsoft's commitment to Responsible AI policies. Module 1: Explore AI solutions and the essentials of responsible AI practices. Discover the potential.

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!