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Generative AI for Engineers
GMUSD66
Credits: 2.00
Prerequisites : Basic understanding of machine learning concepts and proficiency in Python programming
Course Duration : 30 Hours / 12 Weeks
Start Date : 2024-03-28
End Date : 2024-03-30
Seats Remaining : 60
Aim & Summary
The course covers fundamental concepts such as generative adversarial networks (GANs), variational autoencoders (VAEs), and deep reinforcement learning for generative tasks. Participants will explore applications of generative AI in image generation, text generation, music composition, and beyond. Through hands-on projects and practical exercises, participants will gain experience in implementing and fine-tuning generative models using popular frameworks such as TensorFlow and PyTorch. By the end of the course, participants will have the skills to develop custom generative AI solutions tailored to engineering applications.| Week 1 | Introduction to Generative Artificial Intelligence |
| Week 2 | Fundamentals of Generative Models: GANs, VAEs, and Autoregressive Models |
| Week 3 | Understanding Generative Adversarial Networks (GANs) |
| Week 4 | Variational Autoencoders (VAEs) and Latent Space Representation |
QUIZ 1 |
|
| Week 5 | Deep Reinforcement Learning for Generative Tasks |
| Week 6 | Image Generation with GANs: DCGANs, StyleGAN, and Conditional GANs |
| Week 7 | Text Generation with Recurrent Neural Networks (RNNs) and Transformer Models |
| Week 8 | Music Generation with Deep Learning: MIDI Generation and Audio Synthesis |
QUIZ 2 |
|
| Week 9 | Applications of Generative AI in Engineering: Design Optimization, Anomaly Detection |
| Week 10 | Transfer Learning and Fine-Tuning Pretrained Generative Models |
| Week 11 | Ethics and Bias in Generative AI |
| Week 12 | Project Development: Building Custom Generative AI Systems |
PROJECT REPORT |
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