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Generative AI courses

542 Courses

California Institute of Technology Coursera

Gemini in Google Docs - 日本語版

Gemini in Google Docs - 日本語版 Gemini for Google Workspace は、生成 AI 機能へのアクセスをユーザーに提供するアドオンです。動画レッスン、ハンズオンアクティビティ、実用的な例を使用して、Gemini in Google ドキュメントの機能について詳しく説明します。 Gemini を使用して、プロンプトに基づいて文書のコンテンツを生成する方法を学びます。また、Gemini.
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California Institute of Technology Southern New Hampshire University

Generative AI for Educators & Teachers

Generative AI for Educators & Teachers Step into the future of education, problem-solving, and daily life optimization with this specialization in Generative AI. Designed for educators, professionals, and lifelong learners alike, this program equips you with the tools to harness the transformative power of ChatGPT and GPT Vision. Southern New H.
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California Institute of Technology Coursera

Copilot for Cybersecurity

Copilot for Cybersecurity | Coursera Course Title: Copilot for Cybersecurity Description: Discover how Copilot can enhance your cybersecurity skills with our course. Gain a general overview and delve into detailed use case demonstrations tailored for Cybersecurity practitioners eager to augment their skills with Generative AI. Univ.
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California Institute of Technology Coursera

Copilot for Power BI

Copilot for Power BI Course Title: Copilot for Power BI Description: This course explores how to leverage Copilot alongside Power BI to streamline processes and improve work outputs. It provides a general overview and detailed use case demonstrations, ideal for Power BI practitioners looking to enhance their skills with Generative AI. Universi.
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California Institute of Technology Coursera

Generative AI: Advance Your Human Resources (HR) Career

Generative AI: Advance Your Human Resources (HR) Career Are you keen to advance your career in human resources (HR) by using generative AI? In this course, you will learn how GenAI can optimize HR processes and boost professionals' productivity. It discusses the relevance, impact, and use cases of generative AI within the HR domain..
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California Institute of Technology Coursera

Gemini for Google Cloud Learning Path

Course Title: Gemini for Google Cloud Learning Path Description: The Gemini for Google Cloud learning path provides examples of how Gemini can help make engineers of all types more efficient in their daily activities. Gemini offers a natural language chat interface, enabling you to quickly obtain answers to cloud-related questions or receive gu.
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California Institute of Technology Coursera

Generative AI for Project Managers

Generative AI for Project Managers Generative AI knowledge is now an essential skill in project management. According to Business 2 Community, 93% of companies that invest in AI for project management report a positive return on investment. Additionally, generative AI can improve the success rate of projects by around 25%. Reflectin.
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California Institute of Technology Coursera

AI Engineering

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California Institute of Technology Coursera

Generative AI for Developers

Generative AI for Developers Generative AI for Developers Unlock the future of coding with generative AI, and excel your programming skills and deepen your understanding of AI’s transformative power. Perfect for you to integrate advanced AI tools into your workflows, with a blend of technical mastery and ethical insight, ensuring you stay at the.
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California Institute of Technology Coursera

Navigating Generative AI for Leaders

Navigating Generative AI for Leaders Navigating Generative AI for Leaders Created by a CEO for CEOs, this program is your key to unlocking the transformative power of GenAI. It features hands-on labs with access to Google Gemini Pro in a secure, private environment. These labs not only teach you how to use GenAI but also how to apply it to design.
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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!