Master Generative AI for RPA from basics to Advanced

Overview

Generative Artificial Intelligence represents a transformative advancement in the field of machine learning, enabling systems to produce original content such as text, images, music, and even code. Built on sophisticated algorithms, generative AI leverages large-scale data sets and deep learning models, particularly neural networks, to identify patterns and recreate human-like outputs.

Among the most notable generative AI models are transformer-based architectures like GPT and GANs, which have revolutionized creative and analytical processes.

Syllabus

• Introduction to Generative AI
Overview of Generative AI
Applications in RPA (Robotic Process Automation)
History and evolution of generative models
• Fundamentals of Machine Learning
Supervised vs. Unsupervised Learning
Introduction to Neural Networks
Basics of Deep Learning
• Key Generative AI Models
Transformer-based Architectures
Understanding Transformers
Overview of GPT (Generative Pre-trained Transformer)
Generative Adversarial Networks (GANs)
Components of GANs
Applications and Challenges
• Data for Generative Models
Data Preparation and Preprocessing
Leveraging Large-scale Datasets
Importance of Data Quality
• Advanced Topics in Generative AI
Variational Autoencoders (VAEs)
Reinforcement Learning in Generative AI
Fine-tuning and Transfer Learning
• Implementing Generative AI for RPA
Integrating Generative Models into RPA Systems
Use Cases: Text Generation, Image Creation, and Code Generation
Optimizing Workflow Automation with AI
• Tools and Frameworks
Overview of Key Libraries (TensorFlow, PyTorch, etc.)
Software Tools for Implementing Generative Models
Cloud Platforms for Scalable AI
• Ethical and Practical Considerations
Addressing Bias and Fairness in AI
Ensuring Data Privacy
Discussing the Future Impact on Jobs and Industries
• Case Studies and Industry Applications
Real-world Examples of Generative AI in RPA
Innovations and Breakthroughs
Interactive Project: Developing a Simple Generative AI Application
• Conclusion and Future Directions
Summary of Key Concepts
Emerging Trends in Generative AI
Preparing for Further Learning and Research in the Field

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Taught by

Narsi Naidu Chilla

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Subjects

Computer Science