20th August 2023
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Curriculum for Applications of Generative AI
Module 1: Introduction to Generative AI (10 hours)
- Fundamentals of generative AI
- Overview of generative models (GANs, VAEs, etc.)
- Understanding latent space and sampling
- Evaluation metrics for generative models
- Ethical considerations in generative AI
- Hands-on project: Implementing a basic GAN for image generation.
Module 2: Image Generation and Synthesis (10 hours)
- Image generation with GANs
- Conditional GANs for controlled image synthesis
- Image-to-image translation using generative models
- Super-resolution and style transfer with generative AI
- Case studies: Generating realistic faces, landscapes, and art
- Hands-on project: Creating an image-to-image translation model.
Module 3: Natural Language Processing and Text Generation (10 hours)
- Language modeling with RNNs and LSTMs
- Text generation with generative models
- Conditional text generation and story generation
- Text-to-Speech synthesis with generative models
- Applications in automated chatbots and content generation
- Hands-on project: Building a text generation model.
Module 4: Creative Applications of Generative AI (10 hours)
- Music generation using generative models
- Video synthesis and manipulation
- 3D object generation and design
- Interactive and adaptive user interfaces
- Case studies: Applications in art, design, and entertainment
- Hands-on project: Implementing a generative AI application in a creative domain.
For admission to this Professional Certificate course in Applications of Generative AI Course, candidates should have:
- Basic Programming Knowledge
- Database Fundamentals
- Data Analytics Basics
- Mathematics and Statistics (recommended but not mandatory)
- Data Analysis Tools (e.g., Pandas, NumPy, SQL) (recommended but not mandatory)
Upon completion of this course, students will be able to:
- Understand the fundamental concepts and techniques of generative AI.
- Apply generative models to generate new content and enhance existing data.
- Utilize generative AI techniques to solve complex problems in different domains.
- Evaluate and compare different generative models and their applications.
- Apply ethical considerations in the use of generative AI.