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10 Days Realtime LLM Bootcamp
  • Introduction
    • Getting Started
    • Course Syllabus
    • Course Structure
    • Prerequisites
    • Greetings from your Instructors
    • First Exercise (Ungraded)
  • Basics of LLM
    • What is Generative AI?
    • What is a Large Language Model?
    • Advantages and Applications of Large Language Models
    • Bonus Resource: Multimodal LLMs
  • Word Vectors Simplified
    • What is a Word Vector
    • Word Vector Relationships
    • Role of Context in LLMs
    • Transforming Vectors into LLM Responses
      • Neural Networks and Transformers (Bonus Module)
      • Attention and Transformers (Bonus Module)
      • Multi-Head Attention and Further Reads (Bonus Module)
    • Let's Track Our Progress
  • Prompt Engineering
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • Best Practices to Follow in Prompt Engineering
    • Token Limits in Prompts
    • Prompt Engineering Excercise
      • Story for the Excercise: The eSports Enigma
      • Tasks in the Excercise
  • Retrieval Augmented Generation and LLM Architecture
    • What is Retrieval Augmented Generation (RAG)?
    • Primer to RAG Functioning and LLM Architecture: Pre-trained and Fine-tuned LLMs
    • In-Context Learning
    • High level LLM Architecture Components for In-context Learning
    • Diving Deeper: LLM Architecture Components
    • LLM Architecture Diagram and Various Steps
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in Retrieval-Augmented Generation (RAG)
    • Key Benefits of RAG for Enterprise-Grade LLM Applications
    • Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search in Vector Embeddings (Bonus Module)
    • Track your Progress
  • Hands-on Development
    • Prerequisites
    • Dropbox Retrieval App in 15 Minutes
      • Building the app without Dockerization
      • Understanding Docker
      • Using Docker to Build the App
    • Amazon Discounts App
      • How the Project Works
      • Step-by-Step Process
    • How to Run the Examples
  • Live Interactions with Jan Chorowski and Adrian Kosowski | Bonus Resource
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Tracks for Submission
    • Final Submission
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On this page
  • Hybrid Approach
  • Interactive Sessions
  • Addressing Doubts
  • Bonus Resources & Bonus Modules
  • Quick Checks
  1. Introduction

Course Structure

PreviousCourse SyllabusNextPrerequisites

Last updated 1 year ago

Hybrid Approach

This coursework was meticulously designed using a hybrid approach. While the core of the content was delivered through recorded sessions, we also integrated interactive live sessions.

Since the bootcamp cohort is now concluded, we've added the session recordings within the coursework for you to watch them at your own pace.

Interactive Sessions

  • Kick-off Session:

    • This session recording will provide an overview of the course essentials.

  • Fireside Chat with Jan Chorowski | Exploring the Frontiers of Large Language Models:

    • You can find the session recording after the RAG Module as a Bonus Resource.

    • In the session, we will delve into topics like:

      • The potentials and boundaries of LLMs.

      • Overcoming challenges in deploying LLM applications.

      • Strategies for building real-time LLM apps.

      • The intriguing concept of "Learning to Forget" in Large Language Models.

Addressing Doubts

  • Self-Reliance: Our philosophy revolves around nurturing independent learners. While our guidance is continuous, we urge you to harness the expansive online knowledge, exploring answers through search engines and existing literature.

Bonus Resources & Bonus Modules

  • From time to time, you'll find Bonus Resources alongside your coursework.

  • These resources are optional and not essential for quizzes or project completion. They're designed to deepen your understanding, complementing the core material succinctly crafted for those eager to develop their first real-time LLM application. Feel free to explore these additional materials at your leisure for a broader and more detailed learning experience.

Quick Checks

  • If you happen to come across this coursework after the conclusion of the bootcamp cohort, there's no need to be concerned if you haven't been added to the WhatsApp community.

You can find its recording if you couldn't watch the session live.

Communication Channels: Ensure you're part of to ask doubts directly to the framework's creators. This being said, please use Google Search, ChatGPT, GitHub Copilot, or other tools to find the answer yourself. If you're unable to do so, as a best practice, unresolved queries can be raised via GitHub issues, and then their link can be shared on Discord.

WhatsApp Community: If you've shared your contact during registration, you should've been added to a WhatsApp group managed by AI Community IIT Bombay and WnCC IIT Bombay in collaboration with Pathway. If not, please get in touch with .

We wholeheartedly encourage you to proceed with the valuable insights gained from this course and actively participate in the . Should any questions arise, please don't hesitate to seek assistance within the community; your inquiries are always welcome.

here
Pathway's Discord
wncc@iitb.ac.in
Pathway Discord community
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