Generative AI
This course provides a comprehensive introduction to Generative AI, covering its theoretical foundations, practical applications, and ethical considerations. Learners will explore various generative models, their training methods, and real-world use cases while also addressing the challenges and risks associated with AI-generated content.Show more
Course
90 Learners Already enrolledAdvanced
Recommended experience9 chapters
Flexible schedule
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What you will learn
- The fundamental concepts and history of Generative AI
- Theoretical foundations, including probability, neural networks, and deep learning models
- How to build, train, and evaluate generative models like GANs, VAEs, and Transformers
- Real-world applications in text, image, video, and music generation
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There are 6 modules in this course
This course provides a comprehensive introduction to Generative AI, covering its theoretical foundations, practical applications, and ethical considerations. Learners will explore various generative models, their training methods, and real-world use cases while also addressing the challenges and risks associated with AI-generated content.This section introduces the core concepts of Generative AI, explaining how it differs from traditional AI and its significance in modern technology. Learners will explore the history of generative models, from early rule-based systems to advanced deep learning architectures like GANs and Transformers. By the end of this section, students will have a clear understanding of the key models and their real-world impact.
This section delves into the mathematical and computational principles that power Generative AI. Topics include probability distributions, Bayesian inference, and neural networks, which form the backbone of generative models. Students will gain insight into how deep learning techniques enable AI systems to create realistic text, images, and other forms of synthetic data.
- Probability Distributions and Randomness in AIPreview
- Bayesian Inference and Markov ProcessesPreview
- Understanding Information and Optimization in AIPreview
- Overview of Artificial Neural NetworksPreview
- Recurrent Neural Networks (RNNs) and Sequence GenerationPreview
- Variational Autoencoders (VAEs)Preview
In this section, students will learn how generative models are trained, optimized, and evaluated. Key topics include data collection, preprocessing techniques, loss functions, and performance metrics used to assess AI-generated content. Hands-on insights into training models like GANs and VAEs will help learners understand the practical challenges and trade-offs in model development.
This section explores the diverse applications of Generative AI across various industries, including text generation, image synthesis, video creation, and music composition. Learners will analyze real-world use cases, such as AI-driven content creation, deepfake technology, and AI-assisted design. By understanding these applications, students will see the potential of generative models in transforming creative and technical fields.
As Generative AI becomes more powerful, ethical concerns such as bias, misinformation, and intellectual property rights are increasingly relevant. This section examines how generative models can reinforce biases, contribute to deepfake-based fraud, and create legal complexities around AI-generated content. Students will explore existing regulatory frameworks and ethical guidelines to address these challenges responsibly.
The final section looks at the evolving landscape of Generative AI, including emerging architectures, AI-human collaboration, and the sustainability challenges of large-scale models. It also covers regulatory trends and potential limitations in AI governance, highlighting the need for responsible development. By the end of this section, learners will have a forward-looking perspective on where Generative AI is headed and the key challenges that must be addressed.
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