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Generative AI courses

659 Courses

California Institute of Technology Pluralsight

Gemini in Google Docs

Gemini in Google Docs Gemini for Google Workspace is an add-on that provides users with access to generative AI features. This course delves into the capabilities of Gemini in Google Docs using video lessons, hands-on activities, and practical examples. Learn how to use Gemini to generate written content based on prompts. Additionally, explore.
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California Institute of Technology Pluralsight

Image Representation and Processing

Image Representation and Processing In the digital age, the ability to manipulate and understand images is not just a skill but a necessity, especially in fields like computer vision and generative AI. This course is designed to demystify the complexities of digital images, teaching you the foundational skills needed to prepare and utilize image.
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California Institute of Technology Pluralsight

Introduction to Gemini for Google Workspace

Title: Introduction to Gemini for Google Workspace Description: Gemini for Google Workspace is an add-on that provides customers with generative AI features in Google Workspace. In this learning path, you will explore the key features of Gemini and learn how to use them to improve productivity and efficiency within Google Workspace. University:.
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California Institute of Technology Pluralsight

Gemini in Gmail

Gemini in Gmail Gemini in Gmail Gemini for Google Workspace is an add-on that integrates advanced generative AI capabilities into Google Workspace. This mini-course explores the primary features of Gemini and demonstrates how to leverage them to enhance productivity and efficiency within Gmail..
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California Institute of Technology Pluralsight

Gemini in Google Sheets

Gemini in Google Sheets Gemini for Google Workspace is an innovative add-on that offers generative AI capabilities within Google Workspace. In this mini-course, you will explore the essential features of Gemini and discover how to leverage them to enhance productivity and efficiency in Google Sheets. University: Pluralsight Categories: Generative A.
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California Institute of Technology Pluralsight

OpenAI: Prompt Engineering

OpenAI: Prompt Engineering Understanding prompt engineering allows enhancing language model performance by designing effective prompts that produce desired outputs that align with your objectives. This course will teach you how to create effective prompts for OpenAI's ChatGPT. Generative AI enhances worker productivity and has changed how busin.
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California Institute of Technology Pluralsight

Models and Parameters for Developers

Models and Parameters for Developers In today's increasingly competitive landscape, more and more businesses and developers need to leverage the latest AI technologies in order to stay ahead. However, the complexity and breadth of Generative AI models leave many unsure where to begin or how to effectively apply these technologies to real-world sc.
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California Institute of Technology Pluralsight

Generative AI for Improving Writing

Generative AI for Improving Writing Course Overview This course, "Generative AI for Improving Writing," offered by Pluralsight, is designed to enhance your professional and technical writing using Generative AI technology. From the initial draft to the final product, you'll learn how GPT technologies can assist you at every stage. Learning Objec.
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California Institute of Technology Coursera

Copywriting with ChatGPT: Crafting High-Quality Blog Content

Copywriting with ChatGPT: Crafting High-Quality Blog Content Over 80% of bloggers agree that blogging yields results, but crafting high-quality blog content is time-consuming! The average copywriter spends about 4 hours writing a single blog post. For marketing copywriters, AI offers a powerful solution—saving time, enhancing ideati.
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California Institute of Technology Scrimba

Intro to Dall-E and GPT Vision

Intro to Dall-E and GPT Vision Utilize DALL-E to create and edit original images, and employ GPT-4 with Vision to analyze and interpret images in your AI-powered apps! Building projects with generative AI has never looked more amazing! Offered by: Scrimba Categories: Generative AI Courses, DALL-E Courses, Image Generation Courses
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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!