Generative AI

Learn cutting-edge generative AI techniques for creating text, images, audio, and more.

12 Weeks
Online / Offline
English & Tamil
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Course Overview

Stay ahead of the curve with our Generative AI specialization. Learn how Large Language Models (LLMs), diffusion models, and advanced neural architectures are revolutionizing content creation and software development. You will build your own AI-powered tools and understand the mechanics behind applications like ChatGPT and Midjourney.

Skills You Will Master

Generative AI Models Prompt Engineering LLMs & Transformers Diffusion & GAN Models AI Application Development Responsible AI Practices

Course Curriculum

1. Introduction to Generative AI

  • What is Generative AI & its importance
  • Types of AI: Generative vs Discriminative models
  • Applications of Generative AI: art, text, music, code, gaming, healthcare, scientific research
  • Overview of Generative AI tools: OpenAI GPT, DALL·E, MidJourney, Stable Diffusion, Claude AI, Gemini, LLaMA

2. Mathematics & Foundations

  • Probability & Statistics: distributions, conditional probability, Bayes theorem
  • Linear Algebra: vectors, matrices, transformations
  • Calculus: derivatives, gradients, optimization
  • Introduction to Neural Networks basics
  • Information theory basics (entropy, cross-entropy)

3. Machine Learning & Deep Learning Refresher

  • Supervised vs Unsupervised Learning basics
  • Introduction to Neural Networks (ANN, CNN, RNN)
  • Backpropagation & gradient descent
  • Activation functions (ReLU, Sigmoid, Tanh, GELU)
  • Overfitting, regularization, and model evaluation
  • Transfer learning & fine-tuning basics

4. Generative Models

  • Overview of Generative Models: concept and types
  • Probabilistic Models: Gaussian Mixture Models, Hidden Markov Models
  • Variational Autoencoders (VAE)
  • Encoder-decoder architecture
  • Latent space representation
  • Generative Adversarial Networks (GANs)
  • Generator & Discriminator
  • Loss functions & training challenges
  • Variants: DCGAN, StyleGAN, CycleGAN
  • Diffusion Models & Score-based Generative Models
  • Denoising Diffusion Probabilistic Models (DDPM)
  • Latent Diffusion Models (Stable Diffusion)

5. Transformers for Generative AI

  • Introduction to Attention Mechanism
  • Transformer architecture: encoder, decoder, self-attention, multi-head attention
  • GPT models: GPT-3, GPT-4, ChatGPT, LLaMA, Claude, Gemini
  • BERT, T5, Flan-T5, and other transformer variants
  • Fine-tuning transformer models
  • Instruction-tuned LLMs & RLHF (Reinforcement Learning with Human Feedback)
  • Multi-modal transformers (text-to-image, text-to-video)

6. Text Generation & NLP Applications

  • Language models & tokenization
  • Prompt engineering & prompt tuning
  • Text summarization, translation, and paraphrasing
  • Question-answering systems
  • Chatbots & conversational AI
  • Long-context LLMs & memory-augmented LLMs
  • Retrieval-Augmented Generation (RAG) pipelines

7. Image, Audio & Video Generation

  • Image generation using GANs & Diffusion Models
  • Text-to-Image generation (DALL·E, Stable Diffusion, MidJourney)
  • Audio & music generation (WaveNet, Jukebox, Riffusion)
  • Video generation & animation AI tools
  • Style transfer & deepfakes
  • 3D content generation & virtual avatars (DreamFusion, Kaolin, Nerf)

8. Tools & Frameworks

  • Python libraries: TensorFlow, PyTorch, Hugging Face Transformers, Diffusers
  • OpenAI API for text and image generation
  • Diffusion model frameworks: Stable Diffusion, Diffusers library
  • LLM frameworks: LangChain, LlamaIndex, Haystack
  • Visualization & deployment: Streamlit, Gradio, Flask
  • Cloud platforms for AI deployment: AWS, GCP, Azure

9. Ethics, Bias & Responsible AI

  • AI ethics and responsible usage
  • Bias in generative models
  • Copyright & intellectual property considerations
  • Detecting AI-generated content
  • Safety measures for AI deployment
  • AI hallucinations in LLMs and mitigation strategies

10. Generative AI Project Workflow

  • Problem definition & data collection
  • Model selection: GANs, VAE, Transformers, Diffusion Models, or Multimodal LLMs
  • Training & evaluation of generative models
  • Fine-tuning & prompt engineering for LLMs
  • Deployment of AI models using APIs, web apps, or cloud platforms
  • Case studies of real-world applications in text, image, video, music, gaming, and healthcare

11. Advanced Topics & Future Trends

  • Multi-modal AI (text, image, video, audio integration)
  • Reinforcement learning in generative AI
  • LLM agents & autonomous AI systems
  • Generative AI in code generation & software development
  • AI model compression, efficiency, and low-resource deployment

Local Training Campuses & Placement Drives

Classroom & hybrid batches in Coimbatore & Chennai

Coimbatore • Training

Generative AI
Chennai • Training

Generative AI

Recommended Career Reading:

How Generative AI is Changing Software Engineering Jobs Generative AI vs Traditional AI: What Is the Difference and Where Is Each Used?

Take the Next Step in Your Career

Join our comprehensive Generative AI program and gain real-world skills that employers demand.

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