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Generative AI courses

1067 Courses

California Institute of Technology Coursera

Gemini in Google Sheets - 한국어

Gemini in Google Sheets - 한국어 Gemini in Google Sheets - 한국어 Google Workspace를 위한 Gemini는 고객이 Google Workspace에서 생성형 AI 기능을 사용할 수 있도록 하는 부가기능입니다. 이 미니 학습 과정에서는 Gemini의 주요 기능을 살펴보고 이러한 기능으로 Google Sheets의 생산성과 효율성을 향상하는 방법을 알아봅니다..
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California Institute of Technology Coursera

The Role of the CEO in Navigating GenAI

The Role of the CEO in Navigating GenAI This course is designed to guide CEOs and senior leaders on their journey to understanding and leveraging Generative AI. It focuses on the critical role of the CEO in navigating the transformative potential of GenAI. Guest speakers, Andrew Ng (Co-founder and Chairman of Coursera) and Hayden Brown (CEO of.
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California Institute of Technology Coursera

Generative AI and LLMs: Architecture and Data Preparation

Generative AI and LLMs: Architecture and Data Preparation This IBM short course, part of the Generative AI Engineering Essentials with LLMs Professional Certificate, teaches you the basics of using generative AI and Large Language Models (LLMs). It is perfect for existing and aspiring data scientists, machine learning engineers, deep-learning en.
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California Institute of Technology edX

Introduction to Generative AI for Executives and Business Leaders

Introduction to Generative AI for Executives and Business Leaders The Generative AI for Executives and Business Leaders, presented by IBM AI Academy, explores topics and assets that span three core learning areas: strategic essentials, key elements for enterprise AI, and how to put AI to work. This course is designed for executives and business.
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California Institute of Technology Coursera

Generative AI for Business - A Leaders' Handbook

Generative AI for Business - A Leaders' Handbook Generative AI for Business - A Leaders' Handbook is a transformative course specifically designed for business leaders. This course demystifies the complex world of generative AI, distinguishing it from other AI paradigms and delving into core concepts such as Generative Adversarial Networks (GANs).
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California Institute of Technology Coursera

Gen AI Foundational Models for NLP & Language Understanding

Gen AI Foundational Models for NLP & Language Understanding This IBM course will teach you how to implement, train, and evaluate generative AI models for natural language processing (NLP). The course covers a variety of NLP applications including document classification, language modeling, and language translation. Learn the fundamentals of buil.
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California Institute of Technology Coursera

Integrating Applications with Gemini 1.0 Pro on Google Cloud

This short course on integrating applications with Gemini 1.0 Pro models on Google Cloud helps you discover the Gemini API and its generative AI models. The course teaches you how to access the Gemini 1.0 Pro and Gemini 1.0 Pro Vision models from code. It lets you test the capabilities of the models with text, image, and video prompts from an a.
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California Institute of Technology edX

Using GenAI to Automate Software Development Tasks

Using GenAI to Automate Software Development Tasks In this cutting-edge course, you will: Learn how to leverage generative AI to streamline your development workflow Explore AI pair programming tools like CodeWhisperer to boost productivity Master prompt engineering techniques to guide AI models and shape outputs Understand the role o.
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California Institute of Technology Coursera

Fundamentals of Generative AI for Beginners

Fundamentals of Generative AI for Beginners Generative AI is a disruptive technology with immense impact. It is pushing businesses to rethink customer strategies, encouraging chipmakers to meet increased processor demands, and leading academia to alter learning paths and curriculums across various fields of study. This course introduces you to.
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California Institute of Technology edX

Developing Generative AI Applications with Python

Developing Generative AI Applications with Python Generative AI modeling is an in-demand skill for AI model development. Employers now expect generative AI skills to be on an AI engineer’s resume. This hands-on course, which is also part of the IBM AI Applied Professional Certificate, will help you build the generative AI skills you need to stan.
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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!