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Introduction to Generative AI

What is Generative AI?

Definition and overview of generative AI

Applications and real-world examples of generative AI

Fundamentals of Generative AI

Understanding neural networks

Basics of neural networks and their role in generative AI

Introduction to generative models

Types of generative models (e.g., GANs, VAEs)

How generative models learn and generate new data

Working with Generative AI

Data preprocessing for generative AI

Data collection and cleaning for generative AI projects

Training and evaluation of generative models

Techniques for training generative models

Methods for evaluating the performance of generative models

Ethical and Social Considerations

Ethical implications of generative AI

Issues related to biased data and potential misuse of generative AI

Societal impacts of generative AI

Positive and negative effects of generative AI on society and individuals

Future of Generative AI

Advances in generative AI

Current trends and future developments in generative AI

Opportunities and challenges in generative AI

Potential applications and obstacles in the field of generative AI

Introduction to Generative AI: A Beginner's Guide

Welcome to 'Introduction to Generative AI: A Beginner's Guide'! In this course, we will explore the fascinating world of generative artificial intelligence (AI) and provide you with a comprehensive understanding of its fundamentals, applications, working principles, ethical considerations, and future prospects. Generative AI is a rapidly growing field with numerous potential applications, and this course is designed to provide you with a solid foundation to begin your journey into this exciting area of study.

In the first section of the course, we will delve into the basic concepts of generative AI, including its definition, overview, and real-world examples. You will gain insights into what generative AI is, its applications in various domains such as art generation, music composition, and natural language processing, and how it differs from other forms of AI. Following this, we will cover the fundamentals of generative AI, including an understanding of neural networks, the basics of generative models, and the different types of generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). You will also learn how these models learn and generate new data, laying the groundwork for your practical application of generative AI.

Throughout the course, you will also explore the practical aspects of working with generative AI, including data preprocessing, collection, cleaning, training, and evaluation of generative models. Additionally, we will address the ethical and social considerations of generative AI, discussing issues related to biased data, potential misuse, and the societal impacts of generative AI. Lastly, we will examine the future of generative AI, exploring current trends, advances, opportunities, challenges, and potential applications in the field. By the end of this course, you will have a solid understanding of generative AI and be well-equipped to further explore and contribute to this exciting field.

A course that introduce basics of Generative AI. How using Generative AI to create contents for social media can be a force multiplier.  Using Stable Diffusion for generating anime images. How to setup Stable Diffusion, choosing the right anime model for Stable Diffusion and the prompts to generate anime images.  It should cover how to generate scripts for video, putting together the images generated by Stable Diffusion into a video and using voice over.