GenAI Pinnacle Program

Hands-On Learning with Generative AI Projects

  • 10+ GenAI Projects for Real-World Applications
  • Solve Critical Challenges within GenAI Industry
  • Industry Leader’s Perspectives on GenAI Project Complexities
100% Scholarship* Offer
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Join Now- Shape Your GenAI Career!

10+ GenAI Projects to put Theory into Practice

From mastering ChatGPT to crafting RAG systems with your data, these 10+ projects are your gateway to cutting-edge innovation

Learning Objective:

  • Train and evaluate LLMs from scratch
  • Learn LLM best practices and setup
  • Implement advanced computing strategies
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Training Large Language Models

Build Large Language Models (LLMs) like GPT-3.5 from scratch

Learning Objective:

  • Master building a ChatGPT-like LLM
  • Apply pretraining, finetuning, RLHF
  • Learn dialogue-optimized LLM practices
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ChatGPT Model Building

Develop a personalized ChatGPT model, starting from the basics up

Learning Objective:

  • Create a RAG-based QA Chatbot
  • Develop apps end-to-end with LangChain and Streamlit
  • Integrate app UI and backend seamlessly
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Building End-to-End RAG Apps

Craft RAG-based Chatbots and Full-Stack Applications with Integrated Frontend-Backend Synchronization

Learning Objective:

  • Construct Conversational Bots with LLMs including ChatGPT
  • Develop AI Instruments and Agents via LangChain
  • Establish and Manage LLM Applications using LangChain
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Build Conversational Apps and Agents

Create Advanced Conversational Interfaces and Intelligent Agents with LLMs and LangChain Technology

Learning Objective:

  • Enhance search accuracy in RAG systems through reranking
  • Apply RAG system techniques from cutting-edge studies
  • Construct RAG systems for diverse data types including tables, text, and images
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Advanced RAG System Development

Master Precision in RAG Systems Across Various Data Formats with State-of-the-Art Techniques

Learning Objective:

  • Master prompt engineering techniques
  • Build chatbots using ChatGPT API
  • Implement LLMs on private data
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Prompt-Driven LLM Apps

Develop your own LLM Application using Prompt Engineering

Learning Objective:

  • Build RAG systems using LlamaIndex
  • Explore advanced LlamaIndex components
  • Fine-tune embeddings and retrieval
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RAG System Development

Create a production ready RAG systems on your private data

Learning Objective:

  • Efficient LLM finetuning with PEFT
  • Apply LoRA, QLoRA, soft prompting
  • Build instruction-following LLMs
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LLM PEFT Finetuning

Finetuning LLMs using Soft Prompting, Adaptor techniques using PEFT

Learning Objective:

  • Fine-tune Stable Diffusion for datasets
  • Apply best practices in customization
  • Understand Stable Diffusion intricacies
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Customized Diffusion Model Tuning

Finetune your own Stable Diffusion models on custom dataset

Learning Objective:

  • Build Text to Image models with DreamBooth
  • Implement DreamBooth on personal datasets
  • Create context-specific visual models
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DreamBooth Image Creation

Build your own personalized Text to Image models using DreamBooth

Learning Objective:

  • Fine-tune diffusion models with ControlNets
  • Optimize InstructPix2Pix in diffusion models
  • Tailor models for specific datasets
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Diffusion Model Refinement

Finetune Diffusion models using ControlNets and InstructPix2Pix models

GenAI Scholarship Offer

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  • 100% refund on completion of the program within 9 months of enrollment.
  • The refund amount will exclude applicable taxes.
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