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3-Week Building LLMs Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick Off Session at Tryst 2024
  • 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
    • Group Session Recording
  • 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
    • Group Session Recording
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices to Follow
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task for the Module
    • Group Session Recording
  • 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 Architecture with Key Components
    • RAG versus Fine-Tuning and Prompt Engineering
    • Versatility and Efficiency in RAG
    • Key Benefits of using RAG in an Enterprise/Production Setup
    • Hands-on Demo: Performing Similarity Search in Vectors (Bonus Module)
    • Using kNN and LSH to Enhance Similarity Search (Bonus Module)
    • Bonus Video: Implementing End-to-End RAG | 1-Hour Session
    • Group Session Recording
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites
    • 1 – Dropbox Retrieval App
      • Understanding Docker
      • Building the Dockerized App
      • Retrofitting your Dropbox app
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – RAG with Open Source and Running "Examples"
    • 4 (Bonus) – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understanding the Basics
      • Implementation with LlamaIndex and Langchain
    • Building LLM Apps with Open AI Alternatives using LiteLLM
  • Bonus Resource: Recorded Interactions from the Archives
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • Form for Submission
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  • About Pathway
  • Why is Pathway so Fast

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  1. Welcome to the Bootcamp

Know your Educators

Our heartfelt thanks go to these amazing folks who have enriched this course with their expertise and insights:

  • DevClub IIT Delhi Team

  • Adrian Kosowski, CPO at Pathway | Earned PhD at 20 | Former Prof at École Polytechnique and Co-founder of SPOJ | 100+ Research publications

  • Anup Surendran, Head of Product Marketing & Growth at Pathway | Previously Vice President at QuestionPro | Advisor, Texas A&M University

  • Jan Chorowski, CTO at Pathway | PhD in Neural Networks | Co-author with Yoshua Bengio and Geoff Hinton, two of the three Godfathers of AI | Ex – Google Brain, Mila AI

  • Sergey Kulik, Lead Software Research Engineer and Solutions Architect at Pathway | IOI Gold Medalist | Former Head of Service at Yandex

  • Berke Çan Rizai, LLM Research Engineer at Pathway | Former Data Scientist at Getir

  • Mudit Srivastava, Director of Growth at Pathway | Ex - Founding Growth Head at AI Planet

  • Olivier Ruas, Director of Product at Pathway | PhD on kNNs | Ex – Peking University

Special acknowledgment goes to:

  • Mike Chambers, Developer Advocate at AWS, for generously allowing the use of his invaluable educational content from the BuildOnAWS YouTube channel. Together with his colleagues, he has also released courses on LLMs through Deeplearning AI, which are definitely worth exploring.

  • Vijay S Agneeswaran, Senior Director and ML Research Leader at Microsoft, for developing and presenting the session on vision transformers that has been integral to one of the bonus modules within this coursework. His team is at the forefront of this field, and you can discover his work on Google Scholar. If his research captures your interest, consider following him for updates.

Throughout this bootcamp, we've utilized various resources to enrich your educational journey, making every effort to acknowledge contributions appropriately. Should there be any oversight or missed acknowledgment, we encourage you to contact us.

About Pathway

Pathway is the world's fastest data processing engine, supporting unified workflows for batch, streaming data, and LLM applications.

Pathway is the single, fastest integrated data processing layer for real-time intelligence.

  • Mix-and-match: batch, streaming, API calls, including LLMs.

  • Effortless transition from batch to real-time - just like setting a flag in your Spark code.

  • Powered by an extremely efficient and scalable Rust engine, it reduces the cost of any computations.

  • Enabling use cases enterprises crave, making advanced data transformations lightning-fast and easy to implement.

Why is Pathway so Fast

The Pathway engine is built in Rust. We love Rust 🦀. Rust is built for speed, parallel computation, and low-level control over hardware resources. This allows their frameworks to execute maximum optimization for performance and speed.

We also love Python 🐍 – which is why you can write your data processing code in Python, and Pathway will automagically compile it into a Rust dataflow. In other words, with Pathway, you don’t need to know anything about Rust to enjoy its enormous performance benefits! For now, this is a simple enough starting point (that said, feel free to find more details in this ArXiv Paper – your first bonus resource 🙂).

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Last updated 9 months ago

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