Generative AI Fundamentals Specialization, Generative AI: Empowering your creativity and practical applications. Demystifying Deep Learning & the engine behind it.
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 works
Imagine 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 AI
- Generative 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 remember
- Generative 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 Curriculum
Introduction to Generative AI
- What is Generative AI?
- Journey of Generative AI
- How does Generative AI works?
- Applications of generative AI in different sectors and industries
Introduction to Large Language Models (LLM)
- What is LLM?
- How do large language models work?
- General Architecture of Large Language Model
- What can a language model do?
- What are the challenges and limitations of LLM?
- LLM in the AI landscape
- LLM use cases/Application
Generative AI: Prompt Engineering Basics
- What is prompt Engineering?
- Relevance of prompt engineering in generative AI models
- Creating prompts and explore examples of impactful prompts
- Commonly used tools for prompt engineering to aid with prompt engineering
Introduction to Responsible AI
- What is Responsible AI?
- Why it’s important?
- How Google implements responsible AI in their products?
- Google’s 7 AI principles
Generative AI: Impact, Considerations, and Ethical Issues
- Limitations of generative AI and the related concerns
- Identify the ethical issues, concerns, and misuses associated with generative AI
- Considerations for the responsible use of generative AI
- Economic and social impact of generative AI
