Chapter 1: Building LLMs from Scratch - Complete Beginner's Guide & Series Introduction 2025

by The GSM Work
LLM AI Machine Learning Deep Learning ChatGPT Tutorial Series Beginners

Chapter 1: Introduction to the Series

📖 Reading Time: 25 minutes

Imagine asking a computer in 1960: “I’m struggling to learn about technology. Can you help me find resources?”

The computer would respond with something completely irrelevant and confusing. Fast forward to 2025, and you can ask ChatGPT the same question and get a perfectly detailed answer with books, courses, research papers, and personalized learning paths.

What changed? Large Language Models (LLMs).

This is Chapter 1 of our comprehensive series: “Building LLMs from Scratch” - designed specifically for absolute beginners who want to truly understand how ChatGPT, Gemini, and similar AI systems actually work under the hood.

If you’ve been confused by complex AI courses, intimidated by technical jargon, or frustrated by tutorials that skip the fundamentals - this series is for you.

📜fe0f Table of Contents

What Are Large Language Models? (Simple Explanation)

Think of a Large Language Model (LLM) as an extremely smart text predictor that has read millions of books, articles, and websites.

Real-World Example:

When you type on your smartphone keyboard:

  • You write: “See you tom…”
  • Your phone suggests: “tomorrow”

That’s a tiny prediction model.

Now imagine that prediction capability multiplied by millions - understanding context, grammar, facts, reasoning, and able to write essays, code, poems, or answer complex questions.

That’s an LLM!

LLM Name Company What It Does
ChatGPT OpenAI Conversational AI, answers questions, writes code
Gemini Google Multimodal AI, handles text, images, video
Claude Anthropic Helpful, harmless, and honest AI assistant
Llama Meta (Facebook) Open-source LLM anyone can use
GPT-4 OpenAI Most advanced version of ChatGPT

Why Should You Learn About LLMs?

🚀 Reason 1: The Technology is Everywhere

LLMs are already changing your daily life:

Content Creation:

  • Writing emails and reports
  • Creating marketing copy
  • Generating social media posts
  • Drafting articles and blogs

Coding and Development:

  • GitHub Copilot writes code for you
  • ChatGPT debugs your programs
  • AI explains complex code

Business Applications:

  • Customer support chatbots
  • Document summarization
  • Data analysis and insights
  • Automated report generation

Education:

  • Personalized tutoring
  • Assignment help
  • Concept explanations
  • Study material generation

💼 Reason 2: Massive Job Market Growth

The generative AI job market is exploding:

Year Projected Market Size Growth Rate
2023 Baseline -
2024 2x Growth 100% increase
2026 4x Growth 300% increase
2028 6x Growth 500% increase

Real Numbers:

  • Companies worldwide are hiring for LLM-related roles
  • Salaries range from ₹8-50 LPA in India
  • Demand is far exceeding supply of skilled professionals
  • Every industry needs AI expertise now

Job Roles You Can Target:

  • AI/ML Engineer
  • LLM Engineer
  • Prompt Engineer
  • AI Application Developer
  • NLP Specialist
  • AI Research Scientist

💁 Reason 3: Future-Proof Your Career

Technologies come and go (remember the blockchain hype?), but foundational AI skills are here to stay.

What Will Always Matter:

  • ✅ Understanding how AI models work
  • ✅ Problem-solving with AI
  • ✅ Building AI systems from scratch
  • ✅ Computer science fundamentals

What’s Just Hype:

  • ❌ Learning only one specific tool
  • ❌ Following trends without understanding basics
  • ❌ Copy-pasting code without comprehension

Real Example:

In 2020-2021, everyone rushed to learn blockchain. Companies raised millions, promised huge salaries, and the hype was real. By 2023, most blockchain startups shut down due to lack of real use cases beyond cryptocurrency.

Meanwhile, developers who focused on fundamentals (algorithms, system design, data structures) adapted easily to any new technology - whether it was blockchain, cloud computing, or now AI.

💡 Key Takeaway: Learn skills that transfer across technologies, not just one trending tool.

Prerequisites for This Series

✅ What You Need to Know

Before starting, make sure you have:

Programming:

  • ✅ Basic Python (loops, functions, classes)
  • ✅ Understand lists, dictionaries, and basic data structures
  • ✅ Can read and write simple code

Mathematics:

  • ✅ High school algebra (equations, variables)
  • ✅ Basic understanding of graphs and functions
  • ✅ Comfortable with numbers and calculations

Tools:

  • ✅ Python installed on your computer (or use Google Colab)
  • ✅ Jupyter notebook or any code editor (VS Code recommended)
  • ✅ Know how to install packages with pip

❌ What You DON’T Need

We will teach everything from scratch:

  • ❌ No machine learning experience required
  • ❌ No deep learning background needed
  • ❌ No neural network knowledge assumed
  • ❌ No prior AI/ML courses necessary

🛠️ Setting Up Your Environment

Option 1: Local Setup (Recommended)

# Install Python 3.8+ from python.org
# Then install these packages:
pip install torch numpy matplotlib jupyter

Option 2: Google Colab (Free & Easy)

If you’re not comfortable with Python yet, spend 1-2 weeks on Python basics first!

The Evolution: From Simple Chatbots to ChatGPT

📜 1960s: ELIZA - The First Chatbot

Let me show you how primitive early chatbots were.

ELIZA was a “therapist” chatbot:

Human: I am trying to learn about technology but finding it difficult.
ELIZA: Is it because you're trying to learn about technology that you came to me?
Human: Yes
ELIZA: You seem to be quite positive.
Human: Can you help me?
ELIZA: You are sure.

Notice: The conversation goes nowhere. ELIZA just rephrases your words without understanding anything!

🤖 2025: ChatGPT

Now let’s ask the same question to ChatGPT:

Human: I am trying to learn about technology but finding it difficult. 
       Can you provide some resources?

ChatGPT: I'd be happy to help you learn about technology! Here are some resources:

📚 Books:
• "Python Crash Course" by Eric Matthes (beginner-friendly)
• "Code: The Hidden Language" by Charles Petzold (concepts)

💻 Online Courses:
• CS50 from Harvard (free, comprehensive)
• freeCodeCamp (hands-on coding)
• Khan Academy Computer Science

📄 Research & Tutorials:
• Medium programming articles
• YouTube channels: Fireship, Traversy Media
• GitHub Learning Lab (practical projects)

🎯 Tip: Start with one programming language (Python recommended)
and build small projects. Learning by doing is most effective!

What specific area of technology interests you most?

See the difference?

ChatGPT:

  • ✅ Understands your problem
  • ✅ Provides organized, useful resources
  • ✅ Gives actionable advice
  • ✅ Asks follow-up questions

This 60-year evolution is mind-blowing!

Real-World Applications of LLMs

🏭 Where LLMs Are Used Today

Understanding real applications helps you see why learning LLMs matters:

1. Content Creation & Writing

Use Cases:

  • Blog post generation
  • Email drafting and replies
  • Social media content
  • Product descriptions
  • News article summaries

Real Example: Copy.ai and Jasper use LLMs to generate marketing content for thousands of businesses daily, saving hours of human writing time.

2. Code Generation & Development

Use Cases:

  • Autocomplete code (GitHub Copilot)
  • Bug detection and fixes
  • Code explanation and documentation
  • Converting code between languages
  • Writing test cases

Real Example: GitHub reports that developers accept ~30% of Copilot’s suggestions, significantly speeding up development.

3. Customer Service Automation

Use Cases:

  • 24/7 chatbot support
  • Answering FAQs automatically
  • Ticket categorization
  • Sentiment analysis
  • Personalized responses

Real Example: Companies like Shopify use LLM-powered bots that handle 70%+ of customer queries without human intervention.

4. Education & Learning

Use Cases:

  • Personalized tutoring
  • Homework help
  • Concept explanation
  • Quiz generation
  • Language learning

Real Example: Duolingo uses LLMs for conversational practice, Khan Academy for personalized math tutoring.

5. Data Analysis & Insights

Use Cases:

  • Document summarization
  • Report generation
  • Data query in natural language
  • Trend analysis
  • Meeting notes and action items

Real Example: Companies use LLMs to analyze thousands of customer reviews and generate insight reports automatically.

6. Healthcare & Research

Use Cases:

  • Medical literature summarization
  • Patient query responses
  • Drug discovery assistance
  • Clinical note generation
  • Research paper analysis

Real Example: Google’s Med-PaLM uses LLMs to answer medical questions with high accuracy.

💡 What You Can Build After This Series

Once you complete this series, you’ll be able to create:

✅ Personal AI Assistant - Custom chatbot for your specific needs
✅ Code Helper Tool - AI that understands your codebase
✅ Content Generator - Automated blog/social media writer
✅ Study Buddy - AI tutor for any subject
✅ Document Analyzer - Summarize PDFs, articles, reports
✅ Language Translator - Custom translation model
✅ Email Assistant - Smart reply suggestions
✅ Research Tool - Query large document collections

The possibilities are endless once you understand how to build LLMs!

The Problem With Current LLM Courses

When beginners search “learn LLM” or “build LLM from scratch,” they face these issues:

❌ Problem 1: Application-Focused, Not Foundation-Focused

What Most Courses Teach:

  • How to use LangChain
  • How to deploy chatbot apps
  • How to call OpenAI APIs
  • Quick project deployment

What They DON’T Teach:

  • How LLMs actually work internally
  • Mathematics behind attention mechanisms
  • Training process step-by-step
  • Architecture design principles

The Issue: You can deploy an app by copying code, but when an interviewer asks “Explain how self-attention works” or “What are key-query-value matrices?” - you’re stuck.

❌ Problem 2: Too Short and Superficial

Typical Course Structure:

  • “Build LLM in 10 Minutes” (YouTube)
  • “Quick LLM Crash Course” (2 hours)
  • “LLM Basics” (18 videos × 10 minutes each)

Reality:

  • Understanding transformers alone takes hours
  • Tokenization requires deep knowledge
  • Training strategies need detailed explanation
  • Real understanding takes weeks, not hours

❌ Problem 3: Assumes Too Much Prior Knowledge

Many courses jump straight to:

  • Complex transformer architectures
  • Advanced mathematical notation
  • Code without explanation
  • Research paper terminology

Beginners need:

  • Concepts explained from absolute basics
  • Math broken down step-by-step
  • Code with detailed comments
  • Real-world analogies

Why “Building from Scratch” Matters

🎯 The Confidence Factor

Imagine two candidates in an interview:

Candidate A:

“I’ve deployed several LLM applications using LangChain and OpenAI APIs.”

Candidate B:

“I’ve built an LLM completely from scratch - from tokenization to training. I understand every component: embeddings, attention mechanisms, transformer blocks, and training loops. I can explain exactly how GPT generates text.”

Who gets hired? Candidate B, every time.

🧠 Deep Understanding vs Surface Knowledge

Surface Learning Deep Learning (From Scratch)
Copy-paste code Understand every line
Use pre-built tools Build tools yourself
Follow tutorials blindly Know why things work
Stuck when errors occur Debug confidently
Can’t customize Can modify anything
Nervous in interviews Confident with fundamentals

💪 Real-World Advantages

When you build from scratch, you can:

  1. Customize Models

    • Modify architecture for specific tasks
    • Optimize for your use case
    • Add custom features

  2. Debug Effectively

    • Understand where errors come from
    • Fix issues without Googling everything
    • Optimize performance bottlenecks

  3. Explain to Others

    • Teach your team
    • Write technical documentation
    • Lead AI projects

  4. Research and Innovate

    • Read research papers confidently
    • Implement new techniques
    • Contribute to open source

What Makes This Series Different?

✅ 1. Absolute Beginner-Friendly

We assume you know:

  • Basic programming (Python preferred)
  • High school mathematics
  • Basic computer usage

We DON’T assume you know:

  • Machine learning
  • Deep learning
  • Neural networks
  • Transformers
  • Attention mechanisms

Everything is taught from zero!

✅ 2. Step-by-Step Detailed Approach

Other Courses:

Lesson 1: Quick Tokenization Overview
Lesson 2: Embeddings Basics
Lesson 3: Transformers Introduction

Our Series:

Lesson 1: Understanding Language - How Text Works
Lesson 2: From Words to Numbers - Computer's View
Lesson 3: Deep Dive into Tokenization Methods
Lesson 4: Build Your Own Tokenizer (Hands-On)
Lesson 5: Word Embeddings From Scratch
Lesson 6: Position Encodings Explained
... and many more detailed posts

Each post is comprehensive, ensuring you truly understand before moving forward.

✅ 3. Real-World Examples and Analogies

Complex Concepts Made Simple:

When explaining Self-Attention Mechanism, instead of jumping into matrix mathematics, we first use relatable scenarios:

Example: The Party Analogy

Imagine you’re at a party with 20 people (like 20 words in a sentence). When someone mentions “cricket,” your brain automatically pays more attention to:

  • Your friends who play cricket
  • That colleague who watches every match
  • The person who was just talking about IPL

You naturally “tune out” people talking about cooking, movies, or other topics.

That’s exactly what self-attention does! The LLM learns which words in a sentence are related and should “pay attention” to each other.

Only after understanding this concept intuitively do we dive into the mathematics behind it. This approach makes even the most complex topics accessible.

✅ 4. Interactive Learning Materials

Every lesson includes:

  • 📝 Comprehensive written guides
  • 📊 Visual diagrams and flowcharts
  • 💻 Complete code examples with detailed comments
  • 🧪 Practical exercises to test your understanding
  • 📚 Additional reading resources

✅ 5. Hands-On Coding Throughout

You will code:

  • Your own tokenizer
  • Embedding layers
  • Attention mechanisms
  • Transformer blocks
  • Complete training loop
  • Text generation function

Not just theory - practical implementation!

✅ 6. Progressive Difficulty

How We Structure Learning:

Week 1-2: Foundation (Easy)

  • Simple concepts with lots of examples
  • Basic Python implementations
  • Confidence building

Week 3-6: Building Blocks (Moderate)

  • Core LLM components
  • Understanding architecture
  • Hands-on coding increases

Week 7-12: Integration (Challenging)

  • Putting everything together
  • Training full models
  • Debugging and optimization

Week 13+: Mastery (Advanced)

  • Fine-tuning techniques
  • Production considerations
  • Your own projects

Each stage builds on previous knowledge, ensuring you’re never overwhelmed.

What You’ll Learn in This Series

🎓 Module 1: Fundamentals

Topics:

  • What are LLMs and how they evolved
  • How language works for computers
  • Text preprocessing basics
  • Tokenization (word-level, character-level, subword)

You’ll Build:

  • Simple tokenizer from scratch
  • Vocabulary builder
  • Text preprocessor

Practice Exercise:

  • Tokenize your favorite book’s first paragraph
  • Compare different tokenization strategies
  • Build a custom vocabulary for a specific domain

🎓 Module 2: Embeddings

Topics:

  • What are word embeddings?
  • Vector representations of text
  • Word2Vec concepts
  • Position encodings

You’ll Build:

  • Embedding layer from scratch
  • Positional encoding system
  • Embedding visualizations

Practice Exercise:

  • Create embeddings for a small vocabulary
  • Visualize word relationships in 2D space
  • Experiment with different embedding dimensions

🎓 Module 3: Attention Mechanisms

Topics:

  • What is attention?
  • Self-attention step-by-step
  • Multi-head attention
  • Key, Query, Value matrices explained

You’ll Build:

  • Single attention head
  • Multi-head attention module
  • Attention visualizer

Practice Exercise:

  • Implement attention for a simple sentence
  • Visualize attention weights
  • Compare single-head vs multi-head attention

🎓 Module 4: Transformer Architecture

Topics:

  • Transformer blocks explained
  • Feed-forward networks
  • Layer normalization
  • Residual connections

You’ll Build:

  • Complete transformer block
  • Encoder and decoder
  • Full transformer model

Practice Exercise:

  • Assemble all components into working model
  • Test forward pass with sample data
  • Debug and optimize your architecture

🎓 Module 5: Training Your LLM

Topics:

  • Loss functions for language models
  • Training loop implementation
  • Gradient descent for LLMs
  • Optimization techniques

You’ll Build:

  • Complete training pipeline
  • Loss calculation and optimization
  • Model checkpointing system
  • Training monitoring dashboard

Practice Exercise:

  • Train a small model on custom dataset
  • Track loss curves and metrics
  • Experiment with different hyperparameters

🎓 Module 6: Text Generation

Topics:

  • Autoregressive generation
  • Sampling strategies
  • Temperature and top-k/top-p
  • Beam search

You’ll Build:

  • Text generation function
  • Multiple sampling strategies (greedy, beam search, temperature)
  • Interactive chatbot interface

Practice Exercise:

  • Generate text with your trained model
  • Compare different sampling methods
  • Build a simple chatbot demo

Final Project:

  • Create your own mini-GPT model
  • Train it on a dataset of your choice
  • Deploy it as a web application

Who Should Take This Series?

✅ Perfect For:

Students:

  • Engineering/CS students wanting AI careers
  • Mathematics students interested in ML
  • Anyone preparing for AI job interviews

Professionals:

  • Software developers transitioning to AI
  • Data analysts wanting to upskill
  • Product managers needing technical understanding

Enthusiasts:

  • Anyone curious about how ChatGPT works
  • Lifelong learners passionate about AI
  • People wanting to build AI products

⚠️ Not Ideal For:

If You Want:

  • Quick 2-hour crash courses
  • Only application development (no theory)
  • Copy-paste solutions without understanding
  • Shortcuts to “make money with AI”

This series is for serious learners willing to invest time (5-6 months, 2-3 hours daily) to truly master LLMs.

The LLM Job Market Reality

📊 Current Hiring Trends

Companies Actively Hiring for LLM Roles:

Company Type Roles Salary Range (India)
Startups LLM Engineer, AI Developer ₹8-20 LPA
Mid-size Tech ML Engineer, NLP Specialist ₹15-30 LPA
Big Tech Senior AI Engineer, Researcher ₹30-50+ LPA
Research Labs AI Scientist, Research Engineer ₹25-60+ LPA

🌍 Global Opportunities

International Markets:

  • USA: $120k-250k annually
  • Europe: €70k-150k annually
  • Remote roles: Competitive global salaries

💼 What Companies Look For

Must-Have Skills:

  • ✅ Deep understanding of transformers
  • ✅ Hands-on training experience
  • ✅ Python and PyTorch/TensorFlow
  • ✅ Strong mathematical foundation
  • ✅ Ability to explain concepts clearly

Nice-to-Have:

  • Research paper implementations
  • Open-source contributions
  • Personal LLM projects
  • Published work or blogs

Open Source vs Closed Source Models

🔒 Closed Source Models

Examples: GPT-4, Claude Opus

Characteristics:

  • Architecture not publicly available
  • Model weights are secret
  • Use only through APIs
  • Companies don’t share training details

Pros:

  • Usually more powerful (heavy investment)
  • Well-maintained and updated
  • Good customer support

Cons:

  • Can’t customize
  • Expensive API costs
  • No control over updates
  • Privacy concerns (data sent to company)

🔓 Open Source Models

Examples: Llama 3.1, Mistral, Falcon

Characteristics:

  • Full architecture publicly available
  • Model weights downloadable
  • Can run locally
  • Training code often shared

Pros:

  • Free to use
  • Fully customizable
  • Privacy-friendly (run locally)
  • Learn from the code
  • Fine-tune for specific tasks

Cons:

  • Requires technical knowledge
  • Need powerful hardware
  • Self-maintained

📈 The Gap is Closing!

2022: Closed-source models far ahead
2023: GPT-4 dominates, open-source catching up
2024: Llama 3.1 matches GPT-4 performance!

What This Means:

  • Open-source models becoming extremely powerful
  • Learning to build your own LLM is more valuable than ever
  • You can achieve GPT-4 level results with open models

Series Structure and Approach

📚 Learning Philosophy

Our Teaching Principles:

  1. Teach from Absolute Basics

    • No prior knowledge assumed
    • Every concept explained thoroughly
    • Mathematical notation introduced gradually

  2. Nuts and Bolts Approach

    • Understand every component deeply
    • Not just “what” but “why” and “how”
    • Build intuition before diving into code

  3. Learn by Building

    • Implement everything yourself
    • No black-box libraries initially
    • Understand before using shortcuts

  4. Real-World Context

    • Why does this concept matter?
    • Where is this used in practice?
    • Industry applications explained

🎯 Expected Timeline

Realistic Learning Path:

Phase Duration Focus
Foundations 4-6 weeks Basics, tokenization, embeddings
Core Architecture 8-10 weeks Attention, transformers
Training 6-8 weeks Loss functions, optimization
Advanced Topics 4-6 weeks Generation, fine-tuning
Projects 4-6 weeks Build complete applications

Total: 5-7 months with consistent effort (2-3 hours daily)

📖 Learning Resources

This series draws from:

  • Latest research papers and publications
  • Industry best practices from top companies
  • Open-source implementations (PyTorch, TensorFlow)
  • Real production system architectures

Throughout the series, we’ll reference:

  • Foundational AI/ML textbooks and papers
  • Original Transformer research (“Attention is All You Need”)
  • Meta’s Llama architecture insights
  • OpenAI’s GPT evolution documentation
  • Practical code examples from GitHub repositories

Final Thoughts

🚀 The Journey Ahead

Learning to build LLMs from scratch is challenging but incredibly rewarding. Think of this as climbing a mountain:

The climb is steep - You’ll encounter difficult concepts, complex mathematics, and moments of confusion.

The view from top is worth it - You’ll understand one of the most powerful technologies of our time, qualify for high-paying jobs, and have the confidence to build anything AI-related.

💡 What You’ll Gain

Technical Skills:

  • Build complete LLMs independently
  • Understand transformer architecture deeply
  • Implement training pipelines
  • Fine-tune models for specific tasks

Career Benefits:

  • Stand out in job interviews
  • Command higher salaries
  • Lead AI projects at companies
  • Transition to AI research roles

Personal Growth:

  • Problem-solving abilities
  • Mathematical thinking
  • Perseverance and discipline
  • Lifelong learning mindset

🎬 Next Steps

To Continue This Series:

  1. Bookmark this page and check back regularly for new posts
  2. Set aside 2-3 hours daily for focused learning
  3. Follow along with code examples in each post
  4. Join our community (comments section) for discussions and questions
  5. Prepare your environment - Install Python, Jupyter notebooks, and essential libraries

📅 What’s Next?

This is Chapter 1 of the series. More chapters will be published regularly covering:

  • Deep dive into Large Language Models
  • Tokenization and text processing
  • Embeddings and vector representations
  • Attention mechanisms
  • Building your first LLM
  • And much more!

Stay tuned for upcoming posts!

Your Commitment

Before proceeding, ask yourself honestly:

Are you willing to:

  • ✅ Invest 5-7 months of consistent learning?
  • ✅ Spend 2-3 hours daily on this skill?
  • ✅ Push through difficult concepts?
  • ✅ Code along with every example?
  • ✅ Build projects to solidify understanding?

If YES - You’re ready for this journey!
If NO - This might not be the right time (and that’s okay!)

Track Your Progress

🎯 Series Progress Tracker

Use this checklist as you complete each module:

Module 1: Fundamentals

  • □ Understanding LLMs and their evolution
  • □ Text preprocessing fundamentals
  • □ Tokenization methods
  • □ Built my first tokenizer

Module 2: Embeddings

  • □ Word embeddings concept
  • □ Vector representations
  • □ Positional encodings
  • □ Built embedding layer

Module 3: Attention

  • □ Attention mechanism basics
  • □ Self-attention implementation
  • □ Multi-head attention
  • □ Built attention module

Module 4: Transformers

  • □ Transformer architecture
  • □ Encoder-decoder structure
  • □ Built complete transformer

Module 5: Training

  • □ Loss functions
  • □ Training loop
  • □ Trained my first model

Module 6: Generation

  • □ Text generation strategies
  • □ Sampling methods
  • □ Built working chatbot

Bookmark this page and check off items as you progress!

Join the Community

Let’s Learn Together:

  • 💬 Comment below with your motivation for learning LLMs
  • ❓ Ask questions - we respond to every comment
  • 🤝 Share your progress - inspire others and stay accountable
  • 📊 Post your results - share what you built
  • 📢 Spread the word - help others discover this series

Thank You!

Thank you for choosing this series to start your LLM journey. We’re committed to making this the most comprehensive, beginner-friendly, and practical LLM learning series available for free.

🚀 Take Action Now!

Get Started:

  1. 💬 Comment Below - Share your motivation for learning LLMs
  2. 🔖 Bookmark This Page - Come back to track your progress
  3. 📱 Share - Help others discover this free series
  4. ✅ Complete the Checklist - Mark your progress as you learn

Remember: Every expert was once a beginner. Start your journey today! 🎯

Quick Reference

Key Terms Introduced:

Term Simple Definition
LLM Large Language Model - AI that understands and generates text
ChatGPT Popular LLM by OpenAI for conversations
Transformer Architecture that powers modern LLMs
Token Small piece of text (word or part of word)
Embedding Converting words to numbers computers understand
Attention Mechanism that helps LLM focus on relevant words
Training Teaching the model by showing it lots of text
Open Source Code/models freely available to everyone
  • ✅ Basic Python programming (loops, functions, classes)
  • ✅ High school mathematics (algebra, basic calculus)
  • ✅ Familiarity with NumPy (helpful but not required)
  • ✅ Enthusiasm to learn and patience to practice!

How to Follow This Series

📖 Reading Approach

For Each Post:

  1. Read the entire post once without coding
  2. Re-read with a code editor open
  3. Type out all examples (don’t copy-paste!)
  4. Complete the practice exercises
  5. Ask questions in comments

💻 Coding Practice

Best Practices:

  • Type every code example yourself
  • Experiment by changing values
  • Break things and fix them (best learning!)
  • Create your own variations
  • Document your learnings

❓ When You’re Stuck

Remember:

  • Getting stuck is normal and expected
  • Read the explanation again slowly
  • Try simpler examples first
  • Ask in the comments section
  • Take breaks when frustrated

The 20-Minute Rule: If stuck for 20+ minutes, move forward and come back later. Sometimes your brain needs time to process.

Learning Checkpoint

Before Starting the Series, Ensure You Can:

✅ Write basic Python functions
✅ Understand lists, dictionaries, and loops
✅ Read and understand simple code
✅ Use Jupyter notebooks or any Python IDE
✅ Install Python packages using pip

If you’re unsure about any of these, spend a week on Python basics first!

Ready? Let’s build something amazing together! 🚀

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