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Generative AI courses

1093 Courses

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California Institute of Technology FutureLearn

Applied Artificial Intelligence: Natural Language Processing

Delve into the transformative world of Applied Artificial Intelligence focusing specifically on Natural Language Processing (NLP) with this comprehensive course offered through FutureLearn. This course is a part of the Advanced and Applied AI on Microsoft Azure ExpertTrack, designed to equip you with essential AI and machine learning skills while p.
provider FutureLearn
sessions On-Demand
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California Institute of Technology Galileo University

Unlocking the Power of Generative AI with ChatGPT for Higher Education

Unlock the Power of Generative AI with ChatGPT for Higher Education - Dive into the transformative potential of ChatGPT in the educational landscape with this comprehensive course offered by Galileo University through edX. Embrace the future of education and technology by learning how ChatGPT, a cutting-edge conversational language model developed.
provider edX
sessions On-Demand
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California Institute of Technology Universitat Politècnica de València

Herramientas de Inteligencia Artificial para la productividad. Más allá del ChatGPT

Descubre cómo potenciar tu productividad con el apoyo de la Inteligencia Artificial Generativa, en un curso ofrecido por la Universitat Politècnica de València a través de edX. Vas más allá de ChatGPT, explorando herramientas avanzadas de IA para la generación automática de contenido, personalización de mensajes, y automatización de tareas repetiti.
provider edX
sessions On-Demand

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!