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RAG and LLM Bootcamp
  • Welcome to the Bootcamp
    • Course Structure
    • Course Syllabus and Timelines
    • Know your Educators
    • Action Items and Prerequisites
    • Kick-Off Session for the Bootcamp
  • 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: Overview of the Transformer Architecture
      • Attention Mechanism
      • Multi-Head Attention and Transformer Architecture
      • Vision Transformers (ViTs)
    • Bonus: Future of LLMs? | By Transformer Co-inventor
    • Graded Quiz 1
  • Prompt Engineering and Token Limits
    • What is Prompt Engineering
    • Prompt Engineering and In-context Learning
    • For Starters: Best Practices
    • Navigating Token Limits
    • Hallucinations in LLMs
    • Prompt Engineering Excercise (Ungraded)
      • Story for the Excercise: The eSports Enigma
      • Your Task fror the Module
  • 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
    • Graded Quiz 2
  • Hands-on Development
    • Prerequisites (Must)
    • Docker Basics
    • Your Hands-on RAG Journey
    • 1 – First RAG Pipeline
      • Building with Open AI
      • How it Works
      • Using Open AI Alternatives
      • RAG with Open Source and Running "Examples"
    • 2 – Amazon Discounts App
      • How the Project Works
      • Building the App
    • 3 – Private RAG with Mistral, Ollama and Pathway
      • Building a Private RAG project
      • (Bonus) Adaptive RAG Overview
    • 4 – Realtime RAG with LlamaIndex/Langchain and Pathway
      • Understand the Basics
      • Implementation with LlamaIndex and Langchain
  • Final Project + Giveaways
    • Prizes and Giveaways
    • Suggested Tracks for Ideation
    • Sample Projects and Additional Resources
    • Submit Project for Review
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  1. Word Vectors, Simplified

Bonus: Overview of the Transformer Architecture

PreviousTransforming Vectors into LLM ResponsesNextAttention Mechanism

Last updated 11 months ago

This Bonus Module will be more easy-to-understand if you have a grasp on Neural Networks, Backpropagation, Sequence-to-sequence learning, and are comfortable with libraries like NumPy.

In the world of machine learning, Transformers stand out as the Optimus Prime of the technology, much like his role in the Transformers movie saga. Just as Optimus Prime shifts from a truck to a formidable leader, Transformers in machine learning take straightforward inputs and elevate them to intricate, meaningful outputs. They excel in a variety of tasks, from translating languages to generating code.

At the heart of groundbreaking initiatives like AlphaFold 2 and advanced NLP models such as GPT-4 and Llama, Transformers play a pivotal role. To unlock the full scope of what machine learning can achieve, a solid understanding of Transformers is indispensable.

Let's take a closer look at these AI 'Optimus Primes' today.

First things first: Neural Networks and RNNs

Before we get into transformers, let's first understand the background. Let's start by getting a quick understanding of neural networks. Imagine them as the brains inside computers, designed to make sense of all sorts of information, whether it's a picture, a piece of music, or a sentence.

A Closer Look at Neural Networks:

  • Overview: Neural networks, akin to virtual brains, learn from data to perform specific tasks with increasing accuracy.

  • Functioning: Comprised of interconnected layers of neurons, they collaborate to process and make sense of complex data.

Specialized Networks for Varied Applications:

  • Convolutional Neural Networks (CNNs):

    • Role: Acting as the AI's visual processing unit, CNNs excel in interpreting visual data.

    • Functionality: By analyzing images in segmented portions and identifying patterns, CNNs mimic the way humans piece together visual information.

    • Use Cases: From facial recognition systems to deciphering handwritten notes, CNNs are the backbone of image processing applications.

  • Recurrent Neural Networks (RNNs):

    • Role: RNNs serve as the sequential data experts, ideal for processing text and speech.

    • Challenges: Despite their proficiency, RNNs struggle with long data sequences. For instance, in processing a lengthy article, an RNN might struggle to recall the beginning by the time it reaches the end. This difficulty arises from issues known as vanishing gradients (where the network loses track of earlier information) and exploding gradients (where the network's adjustments become too large to manage effectively).

    • Use Cases: Despite these challenges, RNNs are instrumental in tasks that require an understanding of order and sequence, such as translating languages where the meaning depends on word order, or powering chatbots that need to follow a sequence of conversation.

Neural networks are not a one-size-fits-all solution; rather, they are specialized tools. While CNNs are unmatched in visual data interpretation, RNNs shine when it comes to sequential data processing.

This nuanced understanding of neural networks lays the groundwork for appreciating the advancements brought forth by Transformers. As we proceed, keeping these fundamental concepts in mind will enhance our exploration of how Transformers leverage these principles to address more complex challenges in machine learning.

Image Credits: By solihinkentjana via Pixabay