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Generative AI courses

542 Courses

California Institute of Technology Coursera

Understanding Large Language Models in Business

Understanding Large Language Models in Business This course offers a deep dive into Large Language Models (LLMs), exploring their capabilities, applications, challenges, and future potential in the business landscape. Through a blend of theoretical insights and practical examples, learners can review and acquire con.
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California Institute of Technology AWS Skill Builder

Introduction to Generative AI - Art of the Possible

Introduction to Generative AI - Art of the Possible The Introduction to Generative AI - Art of the Possible course provides an introduction to generative AI, use cases, risks, and benefits. With the help of a content generation example, we illustrate the art of the possible. By the end of the course, learners should be able to d.
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California Institute of Technology AWS Skill Builder

AWS で始める生成 AI for Entry (Japanese ONLY) (Na) 日本語実写版

AWS で始める生成 AI for Entry (Japanese ONLY) (Na) 日本語実写版 これから生成 AI を業務で活用していく上で、そもそも生成 AI とは何なのか、どのような技術的背景や、種類があり、業務で活用する上でのユースケースや課題を学習します。また、それらの課題に対して、AWS がどのように活用できるかを学習します。本コースは、AWS における生成 AI の学習の第一歩となる.
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California Institute of Technology AWS Skill Builder

AWS Cloud Quest: Generative AI (Japanese) 日本語版

AWS Cloud Quest: Generative AI (Japanese) 日本語版 Join the AWS Cloud Quest: Generative AI program in Japanese and enhance your skills in a variety of AWS services. Amazon S3 AWS Lambda Amazon EC2 Amazon API Gateway Amazon CloudFront Amazon SageMaker Amazon DynamoDB AWS Cloud 9 Amazon OpenSearch Service Amazon Comprehend.
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California Institute of Technology Treehouse

AI Tools for Designers Course (How To)

AI Tools for Designers Course (How To) Join Dan as he embarks on a captivating journey into the world of Generative AI. Go deep into the revolutionary platforms, from Midjourney and Adobe Firefly to DALL·E and Bing Image Creator, that are reshaping the artistic landscape. Alongside this exploration, the video delves into the ethical implications o.
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California Institute of Technology Codecademy

Streamline Resume Creation with Generative AI Case Study

Title: Streamline Resume Creation with Generative AI Case Study Description: Use ChatGPT to create a custom, well-formatted resume that is tailor-made to a specific job application. Incorporate relevant keywords for improved discoverability. This case study will help you develop skills in using ChatGPT for streamlined resume creation. As a sof.
California Institute of Technology Treehouse

Designing with Generative AI Course (How To)

Designing with Generative AI Course (How To) - Treehouse Join Dan in this course as he uses generative AI to design a website for a bakery, employing the tools and methods highlighted in AI Tools for Designers. What you'll learn Project planning using generative AI tools such as ChatGPT Creating sitemaps wit.
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California Institute of Technology

The AI Engineer Path

The AI Engineer Path Build cutting-edge applications powered by generative AI—an indispensable skill for 2024. Perfect for product teams at startups, agencies, and large corporations. Enhance your capabilities with courses in Generative AI, Object Detection, Vector Databases, and Hugging Face.
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California Institute of Technology Scrimba

Intro to AI Engineering

Intro to AI Engineering A crash course on building web apps powered by generative AI. Learn the basics of AI Engineering while building a project you can add to your portfolio to impress your future employer. Provided by Scrimba. Categories: Generative AI Courses, Prompt Engineering Courses, AI Image Generation Courses
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California Institute of Technology Codecademy

Creating Quick STEAM Activities with Generative AI Case Study

Event: Creating Quick STEAM Activities with Generative AI Case Study Description: Use generative AI to help in creating quick STEAM activities. Create items like lesson handouts, design specifications, material lists, and more. This case study will help you develop your skills in using generative AI to create quick STEAM activities. Taking the.

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!