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Generative AI courses

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California Institute of Technology edX

Mastering Generative AI: Agents with RAG and LangChain

The demand for technical Gen AI skills is on the rise. AI engineers proficient in large language models (LLMs) and frameworks like RAG and LangChain are in high demand. This course, Mastering Generative AI: Agents with RAG and LangChain, equips you with the job-ready skills to attract employers. You'll delve into retrieval-augmented generation (.
California Institute of Technology LinkedIn Learning

IA Generativa: Oportunidades e Considerações para Líderes Empresariais

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California Institute of Technology DataCamp

EU AI Act Fundamentals

Start your journey in Artificial Intelligence with DataCamp's EU AI Act Fundamentals course, specifically tailored for business professionals. This course provides a robust foundation in AI principles while offering in-depth insights into the newly ratified EU AI Act. Participants will explore how to leverage AI technologies to devise impact.
California Institute of Technology DataCamp

Data Skills for Business

Join the 'Data Skills for Business' course by DataCamp and enrich your knowledge in core data concepts. Equip yourself with the skills to answer real-world questions using data and enhance your ability as a data-driven decision-maker within your organization. Data is an invaluable resource in the modern world, making it essential to comprehend a.
California Institute of Technology DataCamp

AI Business Fundamentals

Equip yourself with vital AI knowledge and tools to make an immediate impact in the fast-evolving world of Artificial Intelligence (AI). Our courses are tailored for professionals eager to harness the power of AI and place themselves at the forefront of the AI revolution. With a focus on AI strategy, you'll learn to utilize generative AI an.
California Institute of Technology DataCamp

AI Fundamentals

Embark on an exciting journey through the realms of Artificial Intelligence with DataCamp's AI Fundamentals course. This comprehensive program enables you to conquer AI frontiers and harness the power of generative AI and sophisticated large language models. Designed to lay a strong foundation, this course will propel you into the emerging A.
California Institute of Technology Coursera

GenAI for Automated Financial Reporting

Transform your financial reporting with the innovative power of Generative AI and Large Language Models (LLMs). This course is designed to make financial tasks faster, more efficient, and accurate, offering hands-on projects and expert instruction to develop AI-driven solutions for automating complex financial processes. From generating Acco.
California Institute of Technology Coursera

Generative AI: Turbocharge Mobile App Development

Generative AI: Turbocharge Mobile App Development Embark on a comprehensive journey into the world of generative artificial intelligence (AI) with our course designed to revolutionize mobile app development. Discover how generative AI can be harnessed at every stage of the app development process, including design, content creation, marketing,.
California Institute of Technology Coursera

Generative AI for Mobile App Developers

The global mobile app market is on track to grow over 14% annually by 2030, as reported by Grand View Research. As AI-driven mobile development thrives, there is a rising demand for skilled professionals in this domain. This specialization is ideal for aspiring AI developers, software engineers, web designers, and mobile app developers seeking.
California Institute of Technology LinkedIn Learning

什么是生成式人工智能（Generative AI）

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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!