📘
Winter LLM Bootcamp
  • Welcome to the course. Bienvenue!
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Bootcamp Kick-Off Session
  • Basics of LLMs
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of LLMs
    • Bonus Resource: Multimodal LLMs and Google Gemini
  • Word Vectors, Simplified!
    • What is a Word Vector
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
    • Bonus Section: Overview of the Transformers Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformers Architecture
      • Vision Transformers
    • Graded Quiz 1
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow
    • Token Limits and Hallucinations
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task for the Module
  • Retrieval Augmented Generation (RAG) and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)
    • Primer to RAG: Pre-trained and Fine-Tuned LLMs
    • In-Context Learning
    • High-level LLM Architecture Components for In-Context Learning
    • Diving Deeper: LLM Architecture Components
    • Basic RAG/LLM Architecture Diagram with Key Steps
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Understanding Key Benefits of Using RAG in Enterprises
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App
      • Understanding Docker
      • Building the Dockerized App
      • Retrofitting our Dropbox app
    • Amazon Discounts App
      • How the project works
      • Repository Walkthrough
    • How to Run 'Examples'
    • Bonus Section: Real-time RAG with LlamaIndex and Pathway
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Form for Submission
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Final Project + Giveaways

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Last updated 1 year ago

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Welcome to the Final Stretch of Your Bootcamp Journey!

As we approach the conclusion of this bootcamp, it's time to transform your acquired knowledge into practical applications. The current deadline for submitting the project is 10th March. With two weekends ahead in this schedule, you have a valuable yet concise time frame to create and showcase a meaningful project. Make the most of it!

To guide you, we have selected a range of (which are optional) but you can use them for ideation if needed. However, we encourage you to go beyond and think of a few ideas by looking at the problems around you, as that's one of the better approaches to problem solving. 🌟

Along with that, let's quickly revisit the mandatory requirements for completing the bootcamp.

How to Successfully Complete the Bootcamp

1 - Complete the Quizzes

  • Ensure you complete the required quizzes: one in the and another in the .

2 - Project Development

  • Task: Develop a real-time or static RAG-based LLM application using or streaming data via Pathway with Llamaindex or any other tool that you're comfortable with.

  • Publish: Publish your open-source project on your GitHub with a clear README that includes a video demo. We emphasize this as it makes it easy for course instructors, developers in the community, or your potential employers to evaluate what you've built

  • Submission: Submit the project link through the form provided.

Project Guidelines

  • Option to Modify an Existing Project: If building an LLM application from scratch seems daunting, consider modifying the "Dropbox Retrieval App" example we discussed. Adapt it to create an application with significant business or social value. For inspiration, look at how for a better comprehension of the EU AI Act. This being said a replica of any published project will not be accepted.

  • Project Requirements:

    • Data Source: Your project should use real-time (preferred) or static data sources.

    • Open Source: Ensure your project is open source, hosted on GitHub with a clear README.md file and a License file as a best practice. Ref: / .

    • Documentation: The README.md must include:

      • A demo video link or GIF for a quick overview of your application.

      • A clear description explaining the purpose of your project and how it utilizes the .

      • Instructions for setting up and running the tool.

  • Originality: Your project must be original, not plagiarized, and not a direct replica of any course materials, publicly available projects, or those submitted by peers.

Encouragement for Innovation

  • While using the Dropbox App as a foundation is acceptable, we encourage you to innovate and create something unique. Challenge yourself to develop a project that tests your cognitive abilities and engineering skills.

  • To qualify for your bootcamp certificate, complete the required quizzes.

  • If the idea of creating an LLM application from the ground up (like the one we saw in the Amazon Discounts case) feels overwhelming, you have the option to build upon the "Dropbox Retrieval App" example discussed earlier. By tailoring it to meet specific needs, you can construct an application that holds substantial business or social value.

Before you proceed, ensure you have registered.

  • If you haven't registered yet, now is a good time to do so. Otherwise there won't be any way for us to issue certificates for you or contact you for sending the prizes.

Concurrently, you're expected to build a real-time or static, RAG-based LLM application using the . While doing so, you have to make sure to publish your open-source project on GitHub and submit its link through the form ahead.

What are additional incentives beyond learning for building a novel application? Let's check out.

Registration link: .

🤩
🎉
✌️
ideas/tracks
Vector Embeddings module
RAG module
LLM App
Avril adopted the Dropbox AI project
Adding a License to a Repository
Tutorial for adding MIT License
LLM App
LLM App
https://bit.ly/iit-kanpur-bhu-llm