50000 +
Students Enrolled
(500)
Ratings
20 Weeks
Duration
Our Alumni Work at Top Companies
Generative AI Course Curriculum
It stretches your mind, think better and create even better.
Topics:
Duration: 2 Weeks
0.1 Programming and Mathematics Foundations
Week 1: Python and Deep Learning Basics
Python Essentials
Python Programming Fundamentals
NumPy for Numerical Computing
Data Manipulation with Pandas
Visualization with Matplotlib/Seaborn
Object-Oriented Programming
Deep Learning Prerequisites
Neural Network Basics
Backpropagation Algorithm
Activation Functions
Loss Functions
Optimization Algorithms (SGD, Adam)
Development Environment
Jupyter Notebooks and Google Colab
Git and Version Control
Virtual Environments
GPU Setup and CUDA Basics
Package Management
Mathematics Essentials
Linear Algebra (Matrices, Vectors, Eigenvalues)
Calculus (Derivatives, Chain Rule, Gradients)
Probability and Statistics
Information Theory Basics
Optimization Theory
0.2 Introduction to Deep Learning Frameworks
Week 2: PyTorch and TensorFlow
PyTorch Fundamentals
Tensors and Operations
Automatic Differentiation
Building Neural Networks
Training Loops
Model Saving and Loading
TensorFlow/Keras Basics
Sequential and Functional APIs
Custom Layers and Models
Training and Callbacks
TensorBoard Visualization
Model Deployment
Hugging Face Ecosystem
Transformers Library
Datasets Library
Tokenizers
Model Hub
Spaces and Gradio
Cloud Platforms for AI
Google Colab Pro
AWS SageMaker
Azure ML Studio
Paperspace Gradient
Lambda Labs
Lab Project: Build and train a basic neural network for text generation
Topics:
Duration: 2 Weeks
1.1 Generative AI Landscape
Week 1: Fundamentals and History
What is Generative AI?
Generative vs. Discriminative Models
History and Evolution
Key Breakthroughs and Milestones
Current State of the Art
Future Directions
Types of Generative Models
Autoregressive Models
Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Normalizing Flows
Diffusion Models
Transformer-based Models
Applications Overview
Text Generation and Chatbots
Image Synthesis and Art
Music and Audio Generation
Video Creation
Code Generation
Drug Discovery and Science
Mathematical Foundations
Probability Distributions
Maximum Likelihood Estimation
Bayesian Inference
Latent Variable Models
Sampling Methods
1.2 Core Concepts and Theory
Week 2: Fundamental Techniques
Learning Paradigms
Unsupervised Learning
Self-Supervised Learning
Semi-Supervised Learning
Few-Shot Learning
Zero-Shot Learning
Generation Techniques
Sampling Strategies
Temperature and Top-k/Top-p
Beam Search
Nucleus Sampling
Constrained Generation
Evaluation Metrics
Perplexity
BLEU Score
FID Score
Inception Score
Human Evaluation
Task-Specific Metrics
Challenges in Generative AI
Mode Collapse
Training Instability
Evaluation Difficulties
Computational Requirements
Ethical Considerations
Project: Implement and compare different generative model architectures
Topics:
Duration: 2 Weeks
2.1 Classical Generative Models
Week 1: VAEs and GANs
Variational Autoencoders (VAEs)
Encoder-Decoder Architecture
Latent Space Representation
Variational Inference
KL Divergence
Reparameterization Trick
Conditional VAEs
β-VAE and Disentanglement
Generative Adversarial Networks (GANs)
Generator and Discriminator
Adversarial Training
GAN Objective Functions
Mode Collapse Solutions
Wasserstein GAN
Progressive GAN
StyleGAN Architecture
Hybrid Models
VAE-GAN
Adversarial Autoencoders
BiGAN/ALI
CycleGAN
Domain Adaptation
Training Techniques
Stability Tricks
Spectral Normalization
Self-Attention
Progressive Training
Data Augmentation
2.2 Modern Architectures
Week 2: Flows and Diffusion
Normalizing Flows
Flow-based Models
Change of Variables
Coupling Layers
Autoregressive Flows
Continuous Normalizing Flows
Diffusion Models
Forward and Reverse Process
Denoising Diffusion (DDPM)
Score-Based Models
DDIM Sampling
Guided Diffusion
Latent Diffusion Models
Energy-Based Models
Energy Functions
Contrastive Divergence
Score Matching
Langevin Dynamics
Applications
Comparison and Selection
Model Trade-offs
Quality vs. Speed
Memory Requirements
Use Case Matching
Hybrid Approaches
Lab: Build and train VAE, GAN, and Diffusion models
Topics:
Duration: 3 Weeks
3.1 Transformer Deep Dive
Week 1: Architecture and Attention
Attention Mechanisms
Self-Attention Mathematics
Multi-Head Attention
Scaled Dot-Product Attention
Cross-Attention
Attention Patterns Visualization
Transformer Architecture
Encoder-Decoder Structure
Positional Encodings
Layer Normalization
Feed-Forward Networks
Residual Connections
Improvements and Variants
Sparse Attention (Reformer, Linformer)
Efficient Attention (Performer, Linear Attention)
Flash Attention
Multi-Query Attention
Grouped-Query Attention
Positional Encodings
Sinusoidal Encoding
Learned Embeddings
Rotary Position Embeddings (RoPE)
ALiBi
Relative Position Encodings
3.2 Language Model Architectures
Week 2: Modern LLMs
GPT Family
GPT Architecture Evolution
GPT-3/3.5 Capabilities
GPT-4 Multimodal Features
ChatGPT Training Process
InstructGPT and RLHF
Open Source Models
LLaMA/LLaMA 2 Architecture
Mistral and Mixtral
Falcon Models
MPT (MosaicML)
Qwen and Yi Models
Specialized Architectures
Encoder-Only (BERT, RoBERTa)
Encoder-Decoder (T5, BART)
Retrieval-Augmented Models
Mixture of Experts (MoE)
Sparse Models
Scaling Laws
Chinchilla Scaling
Compute-Optimal Training
Model Size vs. Data Size
Emergent Abilities
Efficiency Considerations
3.3 Training Large Language Models
Week 3: Pre-training and Fine-tuning
Pre-training Objectives
Causal Language Modeling
Masked Language Modeling
Span Corruption
Prefix Language Modeling
UL2 Framework
Training Infrastructure
Distributed Training (DDP, FSDP)
Mixed Precision Training
Gradient Checkpointing
ZeRO Optimization
Pipeline Parallelism
Fine-tuning Techniques
Full Fine-tuning
LoRA and QLoRA
Prefix Tuning
Adapter Layers
Prompt Tuning
Instruction Tuning
Dataset Creation
Constitutional AI
RLHF Pipeline
DPO (Direct Preference Optimization)
RLAIF
Project: Fine-tune an LLM for a specific domain application
Topics:
Duration: 2 Weeks
4.1 Advanced Prompt Engineering
Week 1: Prompting Techniques
Basic Techniques
Zero-Shot Prompting
Few-Shot Learning
Role-Based Prompting
Instruction Following
Format Control
Advanced Strategies
Chain-of-Thought (CoT)
Tree-of-Thoughts (ToT)
Self-Consistency
ReAct Framework
Decomposition Strategies
Prompt Optimization
Automatic Prompt Engineering
Prompt Templates
Meta-Prompting
Prompt Chaining
Dynamic Prompting
Context Management
Context Window Optimization
Information Compression
Relevant Context Selection
Long Context Handling
Memory Systems
4.2 LLM Applications
Week 2: Building with LLMs
Text Generation Applications
Creative Writing
Content Generation
Summarization
Translation
Paraphrasing
Conversational AI
Chatbot Development
Dialogue Management
Personality Design
Context Tracking
Multi-turn Conversations
Knowledge Applications
Question Answering
Information Extraction
Fact Checking
Knowledge Base Construction
Research Assistance
Code Generation
Code Completion
Bug Fixing
Code Explanation
Documentation Generation
Test Generation
Lab
Build a production-ready LLM application with advanced prompting
Topics:
Duration: 2 Weeks
5.1 RAG Architecture
Week 1: Core Components
Document Processing
Text Extraction
Chunking Strategies
Metadata Extraction
Document Parsing
OCR Integration
Embedding Models
Sentence Transformers
OpenAI Embeddings
Instructor Models
Custom Embeddings
Multilingual Embeddings
Vector Databases
Pinecone
Weaviate
Qdrant
ChromaDB
FAISS
Retrieval Strategies
Dense Retrieval
Sparse Retrieval
Hybrid Search
Re-ranking
Query Expansion
5.2 Advanced RAG Techniques
Week 2: Optimization and Deployment
RAG Patterns
Simple RAG
Advanced RAG
Modular RAG
Graph RAG
Multi-Modal RAG
Optimization Techniques
Chunking Optimization
Embedding Fine-tuning
Index Optimization
Caching Strategies
Compression Methods
Quality Improvement
Relevance Scoring
Answer Generation
Citation Addition
Fact Verification
Hallucination Reduction
Production RAG
Scalability Considerations
Update Strategies
Monitoring and Logging
A/B Testing
Performance Metrics
Project
Build an enterprise RAG system with advanced features
Topics:
Duration: 2 Weeks
6.1 Diffusion Model Theory
Week 1: Fundamentals
Diffusion Process
Forward Diffusion
Reverse Diffusion
Noise Schedules
Sampling Algorithms
Score-Based Formulation
DDPM and Improvements
Denoising Diffusion Probabilistic Models
DDIM (Deterministic Sampling)
Improved DDPM
Variance Learning
Progressive Distillation
Conditional Generation
Classifier Guidance
Classifier-Free Guidance
Text Conditioning
Image Conditioning
Multi-Modal Conditioning
Latent Diffusion Models
VAE Encoder/Decoder
Latent Space Diffusion
Stable Diffusion Architecture
Memory Efficiency
Quality vs. Speed Trade-offs
6.2 Stable Diffusion and Applications
Week 2: Practical Implementation
Stable Diffusion Deep Dive
U-Net Architecture
CLIP Text Encoder
VAE Components
Attention Mechanisms
Cross-Attention Layers
Control Methods
ControlNet
IP-Adapter
T2I-Adapter
LoRA for Stable Diffusion
Textual Inversion
Advanced Techniques
Inpainting
Outpainting
Image-to-Image
Style Transfer
Super Resolution
Custom Model Training
DreamBooth
Fine-tuning Strategies
Dataset Preparation
Training Optimization
Model Merging
Lab: Train and deploy custom Stable Diffusion models
Topics:
Duration: 2 Weeks
7.1 GAN-Based Image Generation
Week 1: StyleGAN and Beyond
StyleGAN Architecture
Style-Based Generator
Adaptive Instance Normalization
Mapping Network
Synthesis Network
Perceptual Path Length
StyleGAN Variants
StyleGAN2 Improvements
StyleGAN3 (Alias-Free)
StyleGAN-XL
StyleGAN-T
StyleGAN-Human
Applications
Face Generation
Art Creation
Fashion Design
Architecture Visualization
Medical Imaging
Editing and Manipulation
Latent Space Exploration
StyleCLIP
GANSpace
InterFaceGAN
Semantic Editing
7.2 Specialized Image Models
Week 2: Domain-Specific Generation
Text-to-Image Models
DALL-E 2/3
Midjourney Architecture
Imagen
Parti
eDiff-I
3D Generation
NeRF (Neural Radiance Fields)
3D GANs
Point-E
Shap-E
DreamFusion
Video Generation
Video Diffusion Models
Make-A-Video
Imagen Video
Phenaki
Gen-2
Image Editing Models
InstructPix2Pix
Imagic
Prompt-to-Prompt
DiffEdit
MagicBrush
Project
Build a complete image generation pipeline with editing capabilities
Topics:
Duration: 1 Week
8.1 Audio Generation Models
Speech Synthesis
Text-to-Speech Models
WaveNet
Tacotron
FastSpeech
VALL-E
Voice Cloning
Voice Conversion
Few-Shot Voice Cloning
Emotional TTS
Multi-Speaker Models
Real-Time Systems
Music Generation
MusicLM
AudioLM
Riffusion
MusicGen
MIDI Generation
8.2 Audio Applications
Sound Design
Sound Effects Generation
Foley Automation
Ambient Soundscapes
Audio Restoration
Noise Reduction
Production Tools
Mixing and Mastering AI
Style Transfer
Source Separation
Audio Enhancement
Real-time Processing
Lab
Create an AI music generation application
Topics:
Duration: 2 Weeks
9.1 Code Generation Models
Week 1: Architecture and Training
Code-Specific LLMs
Codex/GitHub Copilot
Code Llama
StarCoder
DeepSeek Coder
WizardCoder
Training Techniques
Code Pre-training
Fill-in-the-Middle
Instruction Tuning for Code
Multi-Language Training
Test-Driven Generation
Code Understanding
Abstract Syntax Trees
Code Embeddings
Semantic Analysis
Bug Detection
Code Review
Specialized Tasks
Code Completion
Code Translation
Refactoring
Documentation
Test Generation
9.2 AI-Assisted Development
Week 2: Practical Applications
IDE Integration
Copilot Integration
Custom Extensions
Real-time Suggestions
Context Management
Multi-file Understanding
Development Workflows
Pair Programming with AI
Code Review Automation
Debugging Assistance
Performance Optimization
Security Analysis
Advanced Applications
Program Synthesis
Automatic Repair
Code Search
API Generation
Migration Tools
Quality and Testing
Code Quality Metrics
Test Coverage
Security Scanning
Performance Profiling
Best Practices
Project
Build an AI-powered development assistant
Topics:
Duration: 2 Weeks
10.1 Vision-Language Models
Week 1: Architecture and Training
Multimodal Architectures
CLIP and Variants
ALIGN
BLIP/BLIP-2
Flamingo
LLaVA
Training Objectives
Contrastive Learning
Image-Text Matching
Masked Modeling
Generative Objectives
Cross-Modal Alignment
Applications
Image Captioning
Visual Question Answering
Image Search
Zero-Shot Classification
Visual Reasoning
Video Understanding
Video-Language Models
Action Recognition
Video Captioning
Temporal Reasoning
Video Search
10.2 Advanced Multimodal Systems
Week 2: Complex Applications
Any-to-Any Generation
Unified Models
CoDi
ImageBind
NExT-GPT
Composable Diffusion
Multimodal Agents
Visual Agents
Embodied AI
Robotics Integration
AR/VR Applications
Interactive Systems
Cross-Modal Generation
Text-to-3D
Audio-to-Image
Image-to-Music
Cross-Modal Retrieval
Style Transfer
Production Systems
Multimodal RAG
Content Moderation
Accessibility Tools
Translation Systems
Creative Tools
Lab: Build a multimodal AI application
Topics:
Duration: 2 Weeks
11.1 Model Optimization
Week 1: Performance Optimization
Quantization Techniques
INT8 Quantization
INT4 and Below
Mixed Precision
Dynamic Quantization
Quantization-Aware Training
Model Compression
Knowledge Distillation
Pruning Strategies
Layer Reduction
Token Merging
Attention Optimization
Inference Optimization
Flash Attention
KV Cache Optimization
Batch Processing
Streaming Generation
Speculative Decoding
Hardware Acceleration
GPU Optimization
TPU Deployment
ONNX Runtime
TensorRT
Mobile Deployment
11.2 Production Deployment
Week 2: Scalable Systems
Serving Infrastructure
Model Serving Frameworks
Load Balancing
Auto-scaling
Caching Strategies
CDN Integration
API Development
RESTful APIs
GraphQL
WebSocket Support
Streaming Responses
Rate Limiting
Monitoring and Observability
Performance Metrics
Quality Monitoring
Cost Tracking
Error Handling
A/B Testing
Edge Deployment
Mobile Deployment
Browser-Based AI
IoT Devices
Offline Capabilities
Privacy-Preserving AI
Project
Deploy a production GenAI system with monitoring
Topics:
Duration: 1 Week
12.1 Ethical Considerations
Bias and Fairness
Bias Detection
Fairness Metrics
Debiasing Techniques
Inclusive Design
Representation Issues
Safety Measures
Content Filtering
Safety Classifiers
Prompt Injection Defense
Output Validation
Use Case Restrictions
Privacy and Security
Data Privacy
Model Security
Adversarial Robustness
Watermarking
Attribution
12.2 Responsible AI
Governance Framework
Ethics Guidelines
Compliance Requirements
Risk Assessment
Audit Trails
Documentation
Transparency
Model Cards
Explainability
Uncertainty Quantification
Limitation Disclosure
User Education
Lab
Implement safety measures for GenAI applications
Topics:
Duration: 1 Week
13.1 Enterprise GenAI
Use Case Identification
Opportunity Assessment
ROI Calculation
Risk Analysis
Pilot Planning
Success Metrics
Implementation Strategy
Build vs. Buy
Vendor Selection
Integration Planning
Change Management
Training Programs
Industry Applications
Healthcare and Life Sciences
Financial Services
Retail and E-commerce
Media and Entertainment
Education
13.2 GenAI Products
Product Development
Market Research
Product Design
User Experience
Pricing Strategy
Go-to-Market
Business Models
SaaS Offerings
API Services
Consulting Services
Custom Solutions
Marketplace Models
Project
Develop a GenAI business case and implementation plan
TOOlS & PLATFORMS
Our AI Programs
Build a complete multi-agent customer service system with: - Natural language understanding - Intent recognition and routing - Knowledge base integration - Escalation handling - Sentiment analysis - Performance monitoring
Develop an AI research agent capable of: - Literature review automation - Data collection and analysis - Report generation - Citation management - Collaborative research - Quality validation
Create an agent system for business process automation: - Workflow orchestration - Document processing - Decision automation - Integration with enterprise systems - Compliance checking - Performance optimization
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100000+ uplifted through our hybrid classroom & online training, enriched by real-time projects and job support.
Come and chat with us about your goals over a cup of coffee.
2nd Floor, Hitech City Rd, Above Domino's, opp. Cyber Towers, Jai Hind Enclave, Hyderabad, Telangana.
3rd Floor, Site No 1&2 Saroj Square, Whitefield Main Road, Munnekollal Village Post, Marathahalli, Bengaluru, Karnataka.
