Programme Parameters
Graduate / Intermediate
3 Days (4.5 Hrs)
Online (e-LMS)
• A basic understanding of the subject area and fundamental programming or scientific concepts.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
This three-day course delves into advanced concepts of generative AI, focusing on GANs and Variational Autoencoders (VAEs), stable training techniques, and applications in creative arts, medicine, and bioinformatics. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
This three-day course delves into advanced concepts of generative AI, focusing on GANs and Variational Autoencoders (VAEs), stable training techniques, and applications in creative arts, medicine, and bioinformatics.
📋 Course Objectives
1. Master the fundamentals of stable training techniques.
2. Put biotechnology techniques to work on real datasets and case studies.
3. Produce a reproducible, portfolio-ready project you can cite in a thesis, paper, or job application.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in biotechnology
• R&D engineers and working professionals applying biotechnology in industry
• Academics and educators building research or teaching capacity in biotechnology
• Data and computational scientists moving into stable training techniques
🚀 Key Learning Outcomes
• Confidence to implement stable training techniques in real projects.
• Tangible, reproducible biotechnology work to show supervisors or employers.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
The Generative Modelling Problem
• Explicit versus implicit density models and where GANs sit
• Latent space, sampling and the intuition behind adversarial training
• Comparing GANs, VAEs and diffusion on the axes that matter
Making Adversarial Training Work
• Generator and discriminator balance and mode collapse
• Loss variants: non-saturating, Wasserstein and gradient penalty
• Spectral normalisation, regularisation and stabilisation tricks
Practical GAN Families
• DCGAN through StyleGAN and progressive growing
• Conditional generation and class or attribute control
• Image-to-image translation including paired and unpaired settings
Judging Generative Output
• FID, IS and their known weaknesses
• Precision and recall for generative models
• Human evaluation protocols and when they are unavoidable
Applications and Risks
• Data augmentation and synthetic data with utility and privacy trade-offs
• Deepfakes, consent and provenance watermarking
• Choosing diffusion over GANs in current practice, and when not to
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Python |
| Covered Tool / Platform | TensorFlow |
| Covered Tool / Platform | PyTorch |
| Covered Tool / Platform | Keras |
| Covered Tool / Platform | Scikit-learn |
| Covered Tool / Platform | Jupyter Notebook |
| Covered Tool / Platform | Google Colab |
| Covered Tool / Platform | Hugging Face |
Frequently Asked Questions
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