What you'll learn

Define Generative AI and its use-cases

Learn about Large Language Models (LLM)

Understand the LLMs in the AI landscape

Get a grasp of Prompt Engineering

Understand the Tools for Prompt Engineering

Learn the fundamentals & importance of Responsible AI

Understand Google's 7 AI principles

Comprehend the impact, considerations, and ethical Issues of Generative AI

Requirements

Enthusiasm and determination to make your mark on the world!

Description

A warm welcome to the Generative AI Fundamentals Specialization course by Uplatz.Generative AI, also known as genAI, is a powerful and exciting field of artificial intelligence focused on creating new content, unlike many other AI systems that primarily analyze or interpret existing data. It can produce diverse outputs like:Text: Poems, code, scripts, musical pieces, emails, letters, etc.Images: Photorealistic portraits, landscapes, abstract art, 3D models, etc.Audio: Music in various styles, sound effects, speech, etc.Video: Realistic simulations, stylized animations, etc.How Generative AI worksImagine generative AI as a highly creative artist trained on massive amounts of data (text, images, etc.). This training allows it to:Learn patterns and relationships within the data. For example, how words typically combine in sentences, how light interacts with objects to create an image, or how musical notes sequence to form melodies.Develop statistical models that capture these patterns. These models act like internal "recipes" for generating new content.Receive prompts or inputs from users, which guide the creative process. This could be a text description, a sketch, or even just a style preference.Use its models and the provided prompts to generate entirely new creations that resemble the training data but are not simply copies.Different techniques used in generative AIGenerative Adversarial Networks (GANs): Two AI models compete, one creating new content, the other trying to distinguish it from real data. This competition refines the generative model's ability to create realistic outputs.Variational Autoencoders (VAEs): Encode data into a latent space, allowing for manipulation and generation of new data points within that space.Transformers: Powerful neural network architectures adept at understanding and generating text, code, and other sequential data.Key points to rememberGenerative AI is still under development, but it's rapidly evolving with amazing potential.While highly creative, it's crucial to remember it's still a machine and the ethical implications of its outputs need careful consideration.It's a powerful tool for various applications like art, design, drug discovery, and more.Generative AI Fundamentals Specialization - Course CurriculumIntroduction to Generative AIWhat is Generative AI?Journey of Generative AIHow does Generative AI works?Applications of generative AI in different sectors and industriesIntroduction to Large Language Models (LLM)What is LLM?How do large language models work?General Architecture of Large Language ModelWhat can a language model do?What are the challenges and limitations of LLM?LLM in the AI landscapeLLM use cases/ApplicationGenerative AI: Prompt Engineering BasicsWhat is prompt Engineering?Relevance of prompt engineering in generative AI modelsCreating prompts and explore examples of impactful promptsCommonly used tools for prompt engineering to aid with prompt engineeringIntroduction to Responsible AIWhat is Responsible AI?Why it's important?How Google implements responsible AI in their products?Google's 7 AI principlesGenerative AI: Impact, Considerations, and Ethical IssuesLimitations of generative AI and the related concernsIdentify the ethical issues, concerns, and misuses associated with generative AIConsiderations for the responsible use of generative AIEconomic and social impact of generative AI