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Generative AI courses

1062 Courses

California Institute of Technology LinkedIn Learning

什么是生成式人工智能（Generative AI）

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California Institute of Technology LinkedIn Learning

KI-Prompting für Content Creation

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California Institute of Technology LinkedIn Learning

Prompt engineering pour la génération de contenu avec l'IA

Plongez au cœur de l'intelligence artificielle avec la formation Prompt engineering pour la génération de contenu avec l'IA, animée par l'expert Madjid Khichane. Cette session, proposée par LinkedIn Learning, vous guidera à travers les techniques essentielles du prompt engineering pour stimuler la création de contenu IA. Que vous soyez passionné.
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California Institute of Technology LinkedIn Learning

Découvrir l'IA générative

Découvrir l'IA Générative | LinkedIn Learning Explorez le monde fascinant de l'IA générative avec ce cours de LinkedIn Learning. En vous inscrivant, vous apprendrez les fondamentaux de l'intelligence artificielle générative, y compris son évolution historique, les modèles et technologies actuels, et comment ils fonctionnent. Découvrez les impl.
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California Institute of Technology LinkedIn Learning

L'IA générative : Les bonnes pratiques de la recherche en ligne

Plongez dans les subtilités de l'intelligence artificielle générative avec une compréhension approfondie des différences entre les moteurs de recherche traditionnels et les moteurs de raisonnement. Cette formation vous guide à travers les meilleures pratiques pour optimiser vos recherches en ligne et maximiser vos compétences en exploration d.
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California Institute of Technology LinkedIn Learning

Was ist generative KI?

Entdecken Sie die faszinierende Welt der generativen KI in diesem umfassenden Kurs von LinkedIn Learning. Hier werden Sie in die grundlegenden Konzepte und historischen Entwicklungen eingeführt. Lernen Sie beliebte Modelle der generativen KI kennen und verstehen Sie, wie diese Technologien funktionieren. Der Kurs beleuchtet a.
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California Institute of Technology LinkedIn Learning

Ihre Arbeit optimieren mit Microsoft Copilot

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California Institute of Technology LinkedIn Learning

Generative KI für Führungskräfte

Lernen Sie in diesem umfassenden Kurs die entscheidenden Fakten und Auswirkungen von generativer KI kennen. Erfahren Sie, wie Sie als Führungskraft Ihr Unternehmen sicher auf dem Weg zu einer innovativen und zukunftsfähigen Ausrichtung führen können. Diese wertvollen Einblicke werden von LinkedIn Learning angeboten und decken eine Vielzahl von.
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California Institute of Technology Pluralsight

Generative AI for Mentees

Unlock the potential of Generative AI in your educational journey with the 'Generative AI for Mentees' course. This transformative learning experience empowers you to harness the capabilities of Generative AI alongside a mentor, enabling you to delve into new subjects effectively. Begin by unraveling the fundamental workings and constraints.
California Institute of Technology LinkedIn Learning

AI for Managers by Microsoft and LinkedIn

Enhance your managerial effectiveness with the learning path "AI for Managers," offered by Microsoft in collaboration with LinkedIn. Managers at every level can gain practical insights on leveraging generative AI to make team meetings and one-on-one sessions more effective, provide impactful feedback, and conduct meaningful career discussi.
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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!