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Generative AI courses

542 Courses

California Institute of Technology AWS Skill Builder

Developing Generative Artificial Intelligence Solutions (Vietnamese)

Developing Generative Artificial Intelligence Solutions (Vietnamese) Trong khóa học này, bạn sẽ khám phá vòng đời của ứng dụng trí tuệ nhân tạo tạo sinh (AI tạo sinh), bao gồm: Xác định trường hợp sử dụng trong kinh doanh Chọn mô hình nền tảng (FM) Cải thiện hiệu suất của FM Đánh giá hiệu suất của FM Triển khai và tác động của việc triể.
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California Institute of Technology AWS Skill Builder

Exploring Artificial Intelligence Use Cases and Applications (Vietnamese)

Exploring Artificial Intelligence Use Cases and Applications (Vietnamese) Trong khóa học này, bạn sẽ khám phá các trường hợp sử dụng trí tuệ nhân tạo (AI), máy học (ML) và trí tuệ nhân tạo tạo sinh (AI tạo sinh) trong thực tế ở nhiều ngành khác nhau. Các lĩnh vực này bao gồm y tế, tài chính, tiếp thị, giải trí, v.v. Bạn cũng sẽ tìm hiểu về khả n.
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California Institute of Technology Coursera

GenAI for Cloud Support Associates

Title: GenAI for Cloud Support Associates Description: This course is designed to introduce Cloud Support Associates to the transformative capabilities of Generative Artificial Intelligence (GenAI). Participants will explore practical strategies to leverage GenAI in various cloud support tasks, enhancing efficiency, productivity, and problem-s.
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California Institute of Technology Hanyang University

Ethical AI for Students

Ethical AI for Students Generative AI (GenAI) is revolutionizing our approach to work and study. However, it also presents significant ethical challenges, including concerns about AI bias, inaccuracy, and plagiarism. These issues make many people cautious about fully embracing GenAI's potential benefits. This course will guide you through the a.
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California Institute of Technology Coursera

Data Analysis, Visualization, and Communication with Copilot

This course will help you to transform complex data into actionable insights and compelling stories using Microsoft Copilot. You'll learn cutting-edge generative AI techniques for data analysis, visualization, and communication, enabling you to uncover hidden patterns, create impactful visuals, and effectively convey your findings to diverse au.
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California Institute of Technology Coursera

Generative AI Advance Fine-Tuning for LLMs

Generative AI Advance Fine-Tuning for LLMs Fine-tuning a large language model (LLM) is crucial for aligning it with specific business needs, enhancing accuracy, and optimizing its performance. In turn, this gives businesses precise, actionable insights that drive efficiency and innovation. This course gives aspiring gen AI engin.
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California Institute of Technology Coursera

Introduction to Microsoft Copilot

Introduction to Microsoft Copilot In this introductory course, you'll embark on a journey into the world of generative AI and Microsoft Copilot. We'll demystify the concepts behind this transformative technology, exploring its potential and limitations. You'll gain a clear understanding of what generative AI is, how it works,.
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California Institute of Technology Coursera

Introduction to Generative AI for Developers With Copilot

Introduction to Generative AI for Developers With Copilot This course introduces developers to generative AI technologies, focusing on their practical applications in software development. You will explore the core concepts of generative AI and understand the basic functionalities and ethical considerations of gener.
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California Institute of Technology Coursera

Data Preparation and Evaluation with Copilot

Data Preparation and Evaluation with Copilot - Coursera This course prepares you to use Microsoft Copilot for data preparation and evaluation tasks. You'll learn how to leverage Copilot's generative AI and natural language processing capabilities to streamline your workflow, ensure data quality, and generate valuabl.
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California Institute of Technology Coursera

Project: Generative AI Applications with RAG and LangChain

Project: Generative AI Applications with RAG and LangChain Get ready to put all your generative AI engineering skills into practice! This guided project will test and apply the knowledge and understanding you’ve gained throughout the previous courses in the program. You will build your own real-world generative AI application. During this course.
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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!