Generative AI courses

1014 Courses

Duet AI for Application Developers

Duet AI for Application Developers In this course, you will discover how Duet AI, the generative AI-powered collaborator from Google Cloud, assists developers in building robust applications. Learn to utilize Duet AI to: Explain code. Recommend Google Cloud services. Generate application code. Engage in a hands-on lab to experience firs.
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provider Google Cloud Skills Boost
pricing Paid Course
duration 1 hour 30 minutes
sessions On-Demand

Duet AI for Cloud Architects

In this course, you will discover how Duet AI, an AI-powered collaborator from Google Cloud, assists administrators in provisioning infrastructure effectively. You'll learn to prompt Duet AI to clarify infrastructure details, deploy GKE clusters, and update existing infrastructure. Through hands-on labs, you'll experience how Duet AI streamlin.
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provider Google Cloud Skills Boost
pricing Paid Course
duration 1 hour
sessions On-Demand

Duet AI for Network Engineers

Duet AI for Network Engineers In this course, you will learn how Duet AI, a generative AI-powered collaborator from Google Cloud, assists network engineers in creating, updating, and maintaining VPC networks. You will understand how to prompt Duet AI to provide specific guidance for your networking tasks, surpassing the assistance typically offe.
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provider Google Cloud Skills Boost
pricing Paid Course
duration 45 minutes
sessions On-Demand

Duet AI for Security Engineers

Title: Duet AI for Security Engineers University: Google Cloud Skills Boost Categories: Generative AI Courses, Duet AI Courses Description: In this course, you will learn how Duet AI, a generative AI-powered collaborator from Google Cloud, assists in securing your cloud environment and resources. The course covers how to deploy example workloa.
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provider Google Cloud Skills Boost
pricing Paid Course
duration 45 minutes
sessions On-Demand

Duet AI for DevOps Engineers

Duet AI for DevOps Engineers In this course, you learn how Duet AI, a generative AI-powered collaborator from Google Cloud, helps engineers manage infrastructure. You learn how to prompt Duet AI to find and understand application logs, create a GKE cluster, and investigate how to create a build environment. Using a hands-on lab.
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Duet AI for end-to-end SDLC

Duet AI for End-to-End SDLC In this course, you will discover how Duet AI, a generative AI-powered collaborator from Google Cloud, can assist you in utilizing Google products and services to develop, test, deploy, and manage applications. With the assistance of Duet AI, you'll learn how to build a web application, fix errors within it, create te.
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Impact, Ethics, and Issues with Generative AI

Impact, Ethics, and Issues with Generative AI In this course, you will explore the impact of generative artificial intelligence (AI) on society, the workforce, organizations, and the environment. This course is suitable for anyone interested in learning about the ethical, economic, and social implications of generative AI and how generative AI c.
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provider edX
pricing Free Online Course (Audit)
duration 3 weeks, 1-3 hours a week
sessions On-Demand

Setting a Generative AI Strategy

In this course, we delve into the potential impact of Generative AI (GenAI) on various facets of business with a particular focus on customer value creation and productivity. We’ll review research from leaders in academia and industry to understand emerging thinking on GenAI’s business impact. Imagine a future where GenAI not only drives busin.
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provider Coursera
pricing Free Online Course (Audit)
duration 6-7 hours
sessions On-Demand

Use Generative AI as Your Thought Partner

Use Generative AI as Your Thought Partner This lesson is part of the comprehensive program, Navigating Generative AI for Leaders. In this session, you'll explore how Coursera CEO, Jeff Maggioncalda, employs generative AI models to become a more effective leader. Jeff will delve into his AI setup, including model choices, and provide practical pr.
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Navigating Generative AI Risks for Leaders

Navigating Generative AI Risks for Leaders This comprehensive course delves into the various risks and concerns associated with Generative AI. Topics include business model risks, inaccuracies in AI-generated content, and significant data security and privacy issues. It emphasizes the crucial role of the CEO in understanding and addressing these.
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provider Coursera
pricing Free Online Course (Audit)
duration 3-4 hours
sessions On-Demand

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!