Learn Generative AI
- Introduction to Generative AI: This module will provide an overview of the field of generative AI and its various applications. Topics covered may include the history of generative AI, common architectures and techniques used in generative models, and an introduction to key concepts such as generative adversarial networks (GANs) and variational autoencoders (VAEs).
- Unsupervised Learning for Generative AI: This module will delve deeper into unsupervised learning techniques used in generative AI, such as clustering and density estimation. Topics covered may include different types of unsupervised generative models (e.g., autoencoders, VAEs, GANs), and how to train and evaluate these models.
- Supervised Learning for Generative AI: This module will explore supervised learning techniques used in generative AI, such as classification and regression. Topics covered may include different types of supervised generative models (e.g., conditional GANs, VAEs), and how to train and evaluate these models.
- Advanced Generative AI Techniques: This module will cover more advanced topics in generative AI, such as reinforcement learning, transfer learning, and interpretability. Topics covered may include different types of advanced generative models (e.g., GANs with reinforcement learning, VAEs with transfer learning), and how to train and evaluate these models.
- Applications of Generative AI: This module will explore various applications of generative AI in fields such as computer vision, natural language processing, and speech recognition. Topics covered may include image synthesis, text generation, and voice synthesis.
- Generative AI in Industry: This module will focus on the practical applications of generative AI in industry, including case studies and best practices for deploying generative models in real-world settings. It will cover important topic such as explainability, bias, and ethical considerations.
- Generative AI Project: Students will complete a final project where they will apply the concepts and techniques covered in the course to a real-world problem. This could include building and training a generative model, evaluating its performance, and presenting their findings to the class.
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