GEN AI
This Generative AI curriculum equips learners with practical knowledge and tools to create intelligent systems that generate text, images, music, and more. From understanding foundational models like GPT and GANs to deploying real-world applications, this course is your launchpad into the creative side of AI.
Course Schedule
Weekend Batch
Days: Saturday & Sunday
Online Training:
7:00 PM - 9:00 PM (2 hours/day)
Offline Training:
- Morning: 9:00 AM - 12:00 PM
- Afternoon: 1:00 PM - 4:00 PM
Weekday Batch
Days: Monday to Friday
Online Training:
7:00 PM - 9:00 PM (2 hours/day)
Offline Training:
- Morning: 9:00 AM - 12:00 PM
- Afternoon: 1:00 PM - 4:00 PM
Why Choose Our Program?
• Project-Based Learning Approach
• Real-World Generative AI Use Cases
• Cutting-Edge Curriculum Using Latest Tools & APIs
• Interactive Demos and Deployment Training
• Certification and Capstone Portfolio
What You'll Learn
•Understand the core concepts of Generative AI and how it's different from traditional AI/ML.
•Build and train neural networks for text and image generation using frameworks like PyTorch and TensorFlow.
•Explore state-of-the-art models including Transformers, GANs, and VAEs.
•Work with Hugging Face, OpenAI, Replicate APIs, and LangChain for building applications.
•Deploy generative applications using Streamlit, Gradio, and cloud platforms.
•Execute real-world capstone projects that integrate multimodal AI generation.
Module 1: Introduction to Generative AI
Module 1: Introduction to Generative AI
Duration : 6 Hours
What is Artificial Intelligence?
What is Generative AI? Real-world Use Cases
Overview of Discriminative vs. Generative Models
Ethical Considerations in Generative AI
Generative AI vs Traditional Machine Learning
Module 2: Foundations of Deep Learning
Module 2: Foundations of Deep Learning
Duration : 8 Hours
Introduction to Neural Networks (Perceptrons, Backpropagation)
Activation Functions and Optimizers
Loss Functions and Evaluation Metrics
Overfitting and Regularization
Introduction to TensorFlow or PyTorch
Module 3: Text Generation using Language Models
Module 3: Text Generation using Language Models
Duration : 10 Hours
Tokenization and Embeddings (word2vec, GloVe)
Recurrent Neural Networks (RNNs), LSTM, GRU
Transformer Architecture and Self-Attention
Pretrained Language Models (GPT, BERT overview)
Prompt Engineering for Text Generation
Module 4: Image Generation Techniques
Module 4: Image Generation Techniques
Duration : 10 Hours
Autoencoders & Variational Autoencoders (VAE)
Introduction to GANs (Generative Adversarial Networks)
Types of GANs: DCGAN, CycleGAN, StyleGAN
Conditional Image Generation
Evaluation Metrics for Images (FID, IS)
Module 5: Multimodal Generative AI
Module 5: Multimodal Generative AI
Duration : 8 Hours
Introduction to Multimodal Learning
Text-to-Image (DALL·E, Stable Diffusion)
Text-to-Audio and Music Generation (Jukebox, AudioLM)
Text-to-Video Overview
Responsible Use & Bias in Multimodal Models
Module 6: Tools & Platforms for Generative AI
Module 6: Tools & Platforms for Generative AI
Duration : 4 Hours
Introduction to Hugging Face, Replicate, and OpenAI APIs
Working with LangChain and Prompt Templates
Fine-tuning vs. Inference
Hosting AI models on Streamlit/Gradio
Version Control for AI Projects (Git, DVC basics)
Module 7: Capstone Project & Certification
Module 7: Capstone Project & Certification
Duration : 10 Hours
Problem Statement Design
Model Selection & Training
Deployment & Presentation
Peer Review & Feedback
Documentation & Report
Certification & Evaluation
Certification & Evaluation
Duration : 4 Hours
Final Capstone Demo Day (Presentation + Q&A)
Evaluation Criteria: Innovation, Technical Accuracy, Deployment, Impact
Quiz-based Assessment (30 MCQs + 2 Coding Questions)
Issuance of Certificate of Completion (with Distinction for top 10%)
Feedback & Career Guidance Session