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Generative AI courses

542 Courses

California Institute of Technology Codecademy

Differentiate for Language and Reading Level With ChatGPT Case Study

Differentiate for Language and Reading Level With ChatGPT Case Study Use ChatGPT to create text at various levels of reading and language level. This can help streamline efforts for creation of educational material. This case study will help you develop your skills in using generative AI as a tool to create texts at different r.
California Institute of Technology Google Cloud Skills Boost

Generative AI Fundamentals - Locales

Generative AI Fundamentals - Locales This course, Generative AI Fundamentals - Locales, is designed specifically for non-English learners. If you prefer the course in English, please enroll in Generative AI Fundamentals. Earn a skill badge by completing the following courses: Introduction to Generative AI In.
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California Institute of Technology Google Cloud Skills Boost

Enterprise Search on Generative AI App Builder

Enterprise Search on Generative AI App Builder Enterprises of all sizes often struggle with making information easily accessible to both employees and customers. Internal documentation is frequently scattered across wikis, file shares, and databases. Similarly, consumer-facing sites tend to offer a vast selection of products, services, and informa.
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California Institute of Technology DataCamp

Understanding Prompt Engineering

Understanding Prompt Engineering Embark on an immersive journey to master ChatGPT, the groundbreaking conversational language model, and revolutionize your business and creative processes. This comprehensive course covers the essentials of prompt engineering, teaching you to construct clear, specific, and open-ended prompts, and advances into s.
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California Institute of Technology Codecademy

Optimizing Code with Generative AI Case Study

Leverage generative AI tools to optimize code readability, maintainability, testability, and resource efficiency, fostering streamlined development. This case study will help you develop your skills in using generative AI as a coding tool. Taking the mindset of a developer, you will use best practices for optimizing code with generative AI. O.
California Institute of Technology Codecademy

Debug Python Code with Generative AI Case Study

Debug Python Code with Generative AI Case Study Generative AI can help you fix bugs, test and simulate code, optimize and improve performance, and much more. This case study will help you develop your skills in using generative AI as a coding tool. Taking the mindset of a developer, you will use best practices for debugging code with an AI syste.
California Institute of Technology Pluralsight

The New Developer: Help Your Engineering Org Navigate Issues of Trust, Agency, & Skill Threat as They Adopt Generative AI

The New Developer: Help Your Engineering Org Navigate Issues of Trust, Agency, & Skill Threat as They Adopt Generative AI In this breakout, we’ll tackle AI adoption inside engineering organizations head-on. You’ll hear about original, empirically grounded research on how software engineers and leaders are thinking about—and rethinking—the role of.
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California Institute of Technology Pluralsight

The Data Sessions: ChatGPT Roundtable Discussion with Pluralsight Experts

The Data Sessions: ChatGPT Roundtable Discussion with Pluralsight Experts Follow along with industry experts as they explore generative AI's transformative power, its creative potential, and the ethics behind its application. Equip yourself with knowledge for the AI era. Finding it challenging to navigate the intricacies of gene.
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California Institute of Technology Pluralsight

LangChain Development

LangChain Development Course | Pluralsight This is an intermediate-level course about LangChain. This course will teach you how to create generative AI applications using this powerful open source platform. Although large-language models have become a must-have offering for every company, they are still complex to develop a.
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California Institute of Technology Pluralsight

OpenAI Model Selection and Integrations

OpenAI publishes several large language models (LLMs), each optimized for different use cases. This course teaches you how to select the right model to balance performance, accuracy, and cost. OpenAI offers customers access to their world-leading large language models and APIs via a straightforward REST interface. The developer's challenge lies.
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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!