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Generative AI courses

542 Courses

California Institute of Technology Southern New Hampshire University

Prompt Engineering for Educators

Prompt Engineering for Educators - Southern New Hampshire University | Coursera Prompt Engineering for Educators Unlock the transformative potential of AI in education with the "Prompt Engineering for Educators" specialization, tailored specifically for the teaching profession. This program will guide educator.
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California Institute of Technology University of Naples Federico II

Introduction to Retrieval Augmented Generation (RAG)

Introduction to Retrieval Augmented Generation (RAG) Introduction to Retrieval Augmented Generation (RAG) In this 2-hour project-based course, you will learn how to import data into Pandas, create embeddings with SentenceTransformers, and build a retrieval augmented generation (RAG) system with your data, Qdrant, and an LLM like Llamafile or Open.
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California Institute of Technology Coursera

Gen AI for Data Privacy & Protection

Gen AI for Data Privacy & Protection Welcome to the 'Generative AI for Data Privacy & Protection' short course, designed to guide you through the cutting-edge intersection of Generative AI and data privacy strategies. In this short course, you'll explore the pivotal role of Generative AI in fortifying data privacy and protection.
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California Institute of Technology Coursera

Content Marketing Using Generative AI

Content Marketing Using Generative AI Content Marketing Using Generative AI The Content Marketing in Generative AI course provides you with a foundational knowledge of digital marketing and how Generative AI continues to revolutionize the way marketing organizations effectively manage content. This course focuse.
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California Institute of Technology Coursera

Generative AI for University Leaders

Generative AI for University Leaders Generative AI is rapidly reshaping industries and the skills that students need to be employable. As the pace of technological change accelerates, universities must innovate to stay competitive and ensure that they are giving students the skills they need to succeed. Generative AI offers the potential to transf.
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California Institute of Technology Coursera

Voice of Change: Communicate with Teams for GenAI Adoption

Voice of Change: Communicate with Teams for GenAI Adoption Did you know that GenAI is transforming the way we think about product strategy and leadership in technology? This shift is not just about technological advances; it's about paving the way for innovation with responsibility and inclusivity at its heart..
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California Institute of Technology edX

Introduction to Generative AI

Introduction to Generative AI Gain a solid foundation in generative AI with this beginner-friendly course. Understand what generative AI is and how it works through interactive lessons Master the art of effective prompting and iterative output refinement Dive deep into major generative models - capabilities and limitations Course Highlight.
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California Institute of Technology Coursera

Advertising in the Age of Generative AI

Advertising in the Age of Generative AI - Coursera The Advertising in the Age of Generative AI course equips you with the essential knowledge, skills, and tools to create and uphold a distinct brand identity that stands out in the competitive market. Learn how to leverage Generative AI to design compelling and effective logos as part of you.
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California Institute of Technology Coursera

Introduction to Generative AI - Art of the Possible

Introduction to Generative AI - Art of the Possible | Coursera The Introduction to Generative AI - Art of the Possible course introduces the concept, use cases, and the importance of generative AI in a business context. This course focuses on business leaders and other decision-makers currently or potentially involved in generative AI projects. B.
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California Institute of Technology Coursera

Building Generative AI Capabilities

Building Generative AI Capabilities The Building a Generative AI Capabilities course provides you with holistic insights into AI in marketing and the impact of internal and external strategic considerations of AI to support and sustain marketing strategies. Learn how to successfully develop an AI strategy and roadmap for your o.
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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!