The Complete Generative AI for Research and Development

Overview

Master Generative AI for Research and Development and learn Prototype creation, optimization and Many More! What you'll learn:

Understand Generative AI for Research and DevelopmentMaster Generative AI Best Practices for Research and DevelopmentLearn the various concepts for Generative ModelsLearn Vector Embedding in Generative AI This course covers many topics in Generative AI for Research and Development.

Generative AI is a revolutionary branch of machine learning that enables machines to create new content, including text, images, music, and even code, based on patterns learned from vast datasets. One of the key components of generative AI is deep learning, particularly neural networks like Generative Adversarial Networks (GANs) and Transformer-based models such as OpenAI’s GPT series.

These models are trained on extensive datasets and use probabilistic algorithms to generate content that closely resembles human-created work. Generative AI is revolutionizing how content is produced.

Writers, designers, and musicians can leverage AI to generate ideas, automate repetitive tasks, or even create complete works of art. AI-powered tools such as ChatGPT, DALL·E, and Stable Diffusion are being widely adopted for content generation, making creative processes more efficient and accessible.

Generative AI continues to evolve, its impact on society will be shaped by how it is regulated and integrated into various industries. While it offers immense opportunities for innovation and efficiency, ethical considerations and responsible AI development must be prioritized.

The future of generative AI lies in striking a balance between technological advancement and societal well-being, ensuring that it serves as an augmentation of human creativity rather than a replacement.

Syllabus

• Introduction to Generative AI
Overview of Generative AI
Key Concepts and Definitions
Applications in Research and Development
• Foundations of Deep Learning
Neural Networks Basics
Introduction to Generative Adversarial Networks (GANs)
Overview of Transformer Models (e.g., GPT)
• Generative Models and their Applications
Types of Generative Models
Applications in Text, Image, Music, and Code Generation
Case Studies of Generative AI in Industry
• Vector Embedding in Generative AI
Understanding Vector Embedding
Role in Content Generation
Practical Applications and Examples
• Tools and Technologies
Overview of Popular Generative AI Tools
Introduction to ChatGPT, DALL·E, and Stable Diffusion
Hands-on with AI Tools for Content Generation
• Best Practices for Generative AI in R&D
Model Training and Optimization
Prototype Creation and Iteration
Integration into Existing Workflows
• Ethical Considerations and Responsible AI Development
Understanding Ethical Risks
Strategies for Mitigating Bias and Discrimination
Frameworks for Responsible AI Usage
• Future Trends and Societal Impact
Evolving Trends in Generative AI
Balancing Technological Advancement with Societal Well-being
The Role of Regulation and Policy
• Course Wrap-up
Summary of Key Learnings
Future Learning Resources
Q&A and Final Discussions

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Taught by

Raj Kumar Thokala

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Subjects

Computer Science