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Generative AI courses

542 Courses

California Institute of Technology AWS Skill Builder

Fundamentals of Machine Learning and Artificial Intelligence (Indonesian)

Fundamentals of Machine Learning and Artificial Intelligence (Indonesian) Dalam kursus ini, Anda akan belajar tentang dasar-dasar machine learning (ML) dan kecerdasan buatan (AI). Anda akan melihat berbagai bentuk hubungan antara AI, ML, deep learning, dan bidang kecerdasan buatan generatif yang sedang berkembang (AI generatif). Anda akan mendapat.
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California Institute of Technology Southern New Hampshire University

Integrating Generative AI into Project Management

Integrating Generative AI into Project Management After taking this course, learners will be able to effectively integrate generative AI tools (Large Language Models, LLMs, like ChatGPT) into their project management workflows to enhance efficiency, communication, and decision-making while maintaining ethical standards and data security. A.
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California Institute of Technology AWS Skill Builder

Prácticas de IA responsable (Español LATAM) | Responsible Artificial Intelligence Practices (LATAM Spanish)

En este curso, aprenderá sobre las prácticas de la IA responsable. En primer lugar, tendrá acceso a una introducción en la que se explicará qué es la IA responsable. Aprenderá a definir la IA responsable, comprenderá los desafíos que la IA responsable intenta superar y explorará las dimensiones fundamentales de la IA responsable. Luego, profun.
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California Institute of Technology Coursera

Content Creation With Generative AI

Content Creation With Generative AI Increasingly, marketers are integrating AI into their marketing operations, enhancing efficiency, creativity, and innovation—don’t get left behind! Unlock the potential of generative AI with this dynamic course designed to help you integrate AI and transform the impact of your content marketi.
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California Institute of Technology Coursera

Gemini in Google Drive - Português Brasileiro

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California Institute of Technology University of Michigan

Leveraging Generative AI for Social Impact Organizations

Leveraging Generative AI for Social Impact Organizations Leveraging Generative AI for Social Impact Organizations focuses on addressing the issue of resource scarcity commonly faced by social impact organizations and highlights how generative AI tools can help free up limited staff capacity from routine administrative tasks. Social impact organi.
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California Institute of Technology University of Naples Federico II

Foundations of Local Large Language models

Course Title: Foundations of Local Large Language Models Description: By the end of this course, learners will have a solid understanding of Large Language Models running locally. You'll be able to set up a local environment using powerful tools to run different LLMs and interact with them both with a web interface as well as APIs. You will expl.
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California Institute of Technology Southern New Hampshire University

Generative AI Assistants

Generative AI Assistants Step into the frontier of digital assistance with the "Generative AI Assistants" specialization, your guide to crafting bespoke AI partners for any sector or specialized need. This course unveils the complexities of tailoring GPT-style language models that intuitively align with your world—be it legal, logistical, or scie.
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California Institute of Technology University of Naples Federico II

GenAI Basics - How LLMs Work

GenAI Basics - How LLMs Work Course: GenAI Basics - How LLMs Work Description: This one-hour course is designed for nontechnical audiences to grasp how GenAI models are trained. You will learn the basics of the data science process through an interactive demo that requires no software installation or downloads. University: University of Naples.
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California Institute of Technology Southern New Hampshire University

Prompt Engineering for Law

Prompt Engineering for Law Step into the cutting-edge intersection of law and technology with the "Prompt Engineering for Law" specialization, designed for legal professionals seeking to harness the power of Generative AI. This course introduces the foundations of Generative AI, exploring its burgeoning role in legal applications from contract a.
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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!