Generative AI

₹9,900.00 (Inc. GST)

Centre for Summer Training

Batch Date

Clear

SKU: N/A Category: Summer Training Program

Generative AI is a rapidly evolving field that focuses on creating new, synthetic data similar to existing datasets. This course provides a structured approach to understanding fundamental concepts, techniques, and tools required to build and deploy generative models. Participants will explore various generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Diffusion Models, and Transformers, and gain hands-on experience in applying them to real-world problems.

Pre-Requisites:

Basic programming knowledge (preferably Python).
Familiarity with Python libraries like NumPy, Pandas, Matplotlib, and TensorFlow/PyTorch.
Some understanding of probability distributions and linear algebra.
Exposure to deep learning concepts (helpful but not mandatory).

Key Learning Outcomes:

By the end of this course, participants will:

Understand Generative AI and its applications across industries (image generation, text synthesis, data augmentation, etc.).
Differentiate between generative models like GANs, VAEs, Diffusion Models, and Transformers.
Gain hands-on experience in implementing and fine-tuning generative models using TensorFlow/PyTorch.
Learn evaluation metrics like Frechet Inception Distance (FID), Inception Score (IS), and Perplexity.
Explore ethical considerations, including bias, misinformation, adversarial attacks, and copyright issues.
Deploy generative models using Hugging Face, Streamlit, or OpenAI API.

Target Audience:

AI enthusiasts and professionals looking to expand their knowledge of Generative AI.
Students and professionals from non-technical backgrounds who want to understand AI-generated content.
Developers and data scientists interested in building real-world applications using Generative AI.

Test & Evaluation:

Assignments: Regular hands-on tasks for skill reinforcement.
Final Project: Developing and presenting a generative AI model.
Assessment: MCQs + practical evaluation.
Certification:
- Certificate of Completion upon successful course completion.
- Project Letter for those who complete the final project.
- No certification for participants who leave the course midway.

Delivery Mode & Duration:

Mode: Online Live Sessions
Total Duration: 120 Hours (60 Hours Live Sessions + 60 Hours Hands-on Assignments)

Additional information

Centre for Summer Training	IIT Kanpur Campus, Online Live
Batch Date	Batch 1: 19th May 2025 – 25th June 2025, Batch 2: 17th June 2025 – 22nd July 2025

Curriculum

Module 1: Introduction to Generative AI

What is Generative AI?
Historical Context and Evolution of AI
Applications in healthcare, finance, creative arts, and gaming
Overview of Generative Models: GANs, VAEs, Diffusion Models, Transformers
Introduction to Prompt Engineering
Ethical Concerns: Bias, Copyright Issues, Adversarial Attacks

Module 2: Core Concepts in AI & Deep Learning

Basics of Machine Learning and AI
Introduction to Neural Networks
Backpropagation and Gradient Descent
Overview of Probability Distributions in AI
Introduction to Transformers and Attention Mechanisms

Module 3: Generative Adversarial Networks (GANs)

Understanding GANs: Generator vs. Discriminator
How GANs Work: Training, Loss Functions, Optimization
Hands-on: Implementing a Basic GAN in TensorFlow/PyTorch
Advanced GAN Variants: DCGAN, WGAN, StyleGAN
Applications: Deepfakes, Super-Resolution, Data Augmentation
Troubleshooting mode collapse, vanishing gradients, and training instability

Module 4: Variational Autoencoders (VAEs)

Introduction to Autoencoders
How VAEs differ from GANs
Mathematical Foundation: Latent Space Representation
Hands-on: Implementing VAEs in Python
Applications: Data Compression, Image Generation, Anomaly Detection

Module 5: Diffusion Models & Advanced Generative Techniques

Introduction to Diffusion Models
Comparison: GANs vs. VAEs vs. Diffusion Models
Hands-on: Implementing a Simple Diffusion Model
Introduction to Stable Diffusion & DALL-E
Text-to-Image Generation: Building a Simple Text-to-Image Model

Module 6: Generative AI in Natural Language Processing (NLP)

Introduction to Large Language Models (LLMs)
Exploring GPT, BERT, LLaMA, and Claude
Fine-Tuning Pretrained LLMs for Domain-Specific Tasks (Healthcare, Finance, etc.)
Hands-on: Building AI-powered Text Summarization & Translation
Introduction to Chatbot Development with OpenAI API & LangChain

Module 7: Fine-Tuning, Evaluation, and Deployment

Fine-Tuning Pretrained Generative Models
Metrics for Generative AI: FID, Inception Score, BLEU, Perplexity
Hands-on: Fine-tuning and evaluating a GAN or VAE
Optimization Techniques to improve model efficiency
Deploying Models on Hugging Face & Streamlit

Module 8: Final Project & Future Trends in Generative AI

Final Project:
- Choose between Image Generation (GANs/Diffusion) or Text Generation (GPT/VAEs)
- Deploying the project on Hugging Face or Streamlit
Future of Generative AI: Multimodal AI, Explainable AI, and AI in Creativity
Course Wrap-Up & Q&A

All Courses

Generative AI

Pre-Requisites:

Key Learning Outcomes:

Target Audience:

Test & Evaluation:

Delivery Mode & Duration:

Additional information

Curriculum

Module 1: Introduction to Generative AI

Module 2: Core Concepts in AI & Deep Learning

Module 3: Generative Adversarial Networks (GANs)

Module 4: Variational Autoencoders (VAEs)

Module 5: Diffusion Models & Advanced Generative Techniques

Module 6: Generative AI in Natural Language Processing (NLP)

Module 7: Fine-Tuning, Evaluation, and Deployment

Module 8: Final Project & Future Trends in Generative AI

Enquiry

All Courses

Generative AI

Pre-Requisites:

Key Learning Outcomes:

Target Audience:

Test & Evaluation:

Delivery Mode & Duration:

Additional information

Curriculum

Module 1: Introduction to Generative AI

Module 2: Core Concepts in AI & Deep Learning

Module 3: Generative Adversarial Networks (GANs)

Module 4: Variational Autoencoders (VAEs)

Module 5: Diffusion Models & Advanced Generative Techniques

Module 6: Generative AI in Natural Language Processing (NLP)

Module 7: Fine-Tuning, Evaluation, and Deployment

Module 8: Final Project & Future Trends in Generative AI

Enquiry

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