Generative Artificial Intelligence
What You will Learn
- Dive deep into GANs, a popular class of generative models, and learn how they work.
- Understand how generative models are used in language-related tasks, including text generation, graphics, images, videos, etc.
- Stay updated on the latest research and advancements in generative AI for cutting-edge knowledge.
Course Duration
- 3 Months
- 12 Weeks
- 24 Classes
- 48 Hours
Course Perks
- Individual Consultancy
- Assessments & Certification
- Guest Lectures
- Online Webinar Sessions
Generative Artificial Intelligence
Module 1: Introduction to Generative AI
- Overview of Artificial Intelligence and Machine Learning
- Understanding Generative Models
- Importance and Applications of Generative AI
Module 2: Fundamentals of Probability and Statistics
- Probability Distributions
- Maximum Likelihood Estimation (MLE) and Bayesian Inference
- Statistical Concepts Relevant to Generative Models
Module 3: Introduction to Neural Networks
- Basics of Neural Networks
- Activation Functions, Loss Functions, and Optimization
- Training Neural Networks for Generative Tasks
Module 4: Generative Adversarial Networks (GANs)
- Understanding GAN Architecture
- Training GANs for Image Generation
- Applications of GANs in Image Synthesis
Module 5: Variational Autoencoders (VAEs)
- Introduction to Autoencoders
- Variational Inference and Latent Space
- Applications of VAEs in Data Generation
Module 6: Sequence Generation with Recurrent Neural Networks (RNNs)
- Overview of Recurrent Neural Networks
- Training RNNs for Sequence Generation
- Applications in Natural Language Processing (NLP)
Module 7: Reinforcement Learning for Generative Tasks
- Introduction to Reinforcement Learning
- Policy Gradient Methods for Generative Tasks
- Applications in Game Playing and Creative Tasks
Module 8: Transfer Learning and Fine-tuning
- Leveraging Pre-trained Generative Models
- Fine-tuning for Specific Tasks
- Real-world Applications and Case Studies
Module 9: Ethical Considerations in Generative AI
- Bias and Fairness in AI
- Ethical Challenges in Generative Model Deployments
- Responsible AI Practices
Module 10: Future Trends and Advanced Topics
- Cutting-edge Research in Generative AI
- Emerging Trends and Technologies
- Opportunities and Challenges in the Field
Final Project:
- Implementation of a Generative AI project
- Presentation and Evaluation
This course outline can be adjusted based on the experienced level such as beginner, intermediate, advanced as well as the student’s background. Practical exercises, case studies, and real-time digital-world implications should complement the theoretical components.
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