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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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  1. Introduction

Prerequisites

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Last updated 1 year ago

This bootcamp is tailored to accommodate participants with minimal prerequisites, ensuring it's approachable for individuals from various backgrounds. For those in business functions or educational domains outside of programming, the course's theoretical aspects are still largely comprehensible. This understanding will aid in making more informed decisions and better use of these technologies.

However, having a grasp of the listed prerequisites will greatly facilitate your journey through the entire curriculum, including bonus resources and the hands-on development module.

1 – Python Foundations: If you're new to Python or need a refresher, here are some free Python tutorials to get you started.

2 – Docker Fundamentals: Docker or containerization resolves the "it works on my machine" issue. Hence, it's great not just for this bootcamp but also for enabling you to explore open-source frameworks in general. Here are a couple of resources you can consider:

3 – Open Source Familiarity: While previous experience with open-source projects can be beneficial, it isn't essential. It's worth noting that many routine challenges, such as publishing a local project on GitHub or using widely-accepted APIs, can often be resolved through a simple Google search or by consulting the relevant documentation. While these challenges won't present themselves at every turn, independently navigating them will equip you with the skills to devise impactful solutions in the future.

https://docs.python.org/3/tutorial/index.html
https://www.py4e.com/
Docker Tutorial on FreeCodeCamp
How to Dockerize your Python Applications