LLM Learning Plan.md

Learning Plan: Understanding and Implementing LLMs (Large Language Models)

Audience: Software engineer with a computer science background
Goal: Learn modern LLMs on a technical and foundational level with practical implementation
Time Commitment: 1–2 hours/day
Approach: Mixed theory and coding, compact but deep plan
Focus: Free resources prioritized, including YouTube, articles, and open-access papers

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📅 Weekly Timeline Overview

Week	Focus Area
1	Fundamentals of ML & Deep Learning
2	NLP Basics & Word Embeddings
3	Transformers & Self-Attention
4	Language Modeling & Pretraining Objectives
5	Implementing Mini Language Models
6	Training & Fine-tuning Transformers
7	Serving, Prompting, and Ecosystem
8	Advanced Architectures & Research Papers
9	Capstone Implementation & Research Readings

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📘 Week 1: Machine Learning & Deep Learning Basics

Theory

• Article: Machine Learning Crash Course – Google
• YouTube: 3Blue1Brown - Neural Networks
• Book (Optional): Deep Learning Book – Goodfellow – Chapter 6–7

Practice

• Colab Notebook: Implement a simple neural net with PyTorch Quickstart
• Kaggle: Intro to Deep Learning

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📘 Week 2: NLP Fundamentals & Embeddings

Theory

• Article: CS224n Notes: Word Vectors
• YouTube: Lilian Weng - NLP from Scratch

Practice

• Implement: Word2Vec skip-gram (simplified)
• Code: Use gensim to load embeddings and do similarity comparisons
• Colab: Word2Vec from Scratch

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📘 Week 3: Transformers & Self-Attention

Theory

• Article: The Illustrated Transformer
• Paper: Attention Is All You Need
• YouTube: Attention Explained – Jay Alammar

Practice

• Code: Build a transformer encoder layer using PyTorch
• Colab: Transformer from Scratch – Harvard NLP

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📘 Week 4: Language Modeling & Pretraining

Theory

• Article: Lil'Log on Language Modeling
• Paper: GPT-1: Improving Language Understanding

Practice

• Implement: A tiny character-level language model (use Andrej Karpathy’s nanoGPT)
• Experiment: Train it on small text datasets (e.g. Shakespeare, Python code)

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📘 Week 5: Implementing a Mini Language Model

Theory

• YouTube Series: Andrej Karpathy’s GPT From Scratch
• Repo: minGPT

Practice

• Follow Karpathy’s notebook and implement a transformer step by step
• Train it on simple text corpus and experiment with generation

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📘 Week 6: Training and Fine-tuning

Theory

• Article: HuggingFace Transformers Course (Free)
• Video: Fine-tuning GPT-2 – HuggingFace

Practice

• Colab: Fine-tune GPT-2 on your own dataset using HuggingFace
• Tool: Try transformers.Trainer and datasets

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📘 Week 7: Prompting, Serving, and Tools

Theory

• Article: Prompt Engineering Guide
• Article: LangChain Overview

Practice

• Try: Prompting OpenAI API (or HuggingFace models)
• Experiment: Build a chatbot or document Q&A with LangChain or LlamaIndex

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📘 Week 8: Advanced LLM Architectures & Research

Theory

• Papers:

  • GPT-3
  • PaLM
  • LLM Survey 2023

• YouTube: Yannic Kilcher’s summaries of major papers

Practice

• Read and summarize papers
• Compare architectures using visual diagrams (e.g. llama vs. gpt vs. bert)

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📘 Week 9: Capstone Project & Research

Goal

• Implement a mini LLM pipeline from scratch (train → inference)
• Pick a paper to replicate
• Optionally, write a blog post or dev diary of your journey

Suggestions

• Build a domain-specific chatbot
• Try model distillation or quantization
• Submit to HuggingFace Spaces

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✅ Resources & Tools Summary

Type	Resource
Framework	PyTorch
NLP Library	HuggingFace Transformers
Notebook	Google Colab
Datasets	HuggingFace Datasets
Books	Deep Learning by Goodfellow

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🧠 Tips for Success

• Use Obsidian or Notion to journal your learning.
• Take notes from every video/article.
• Push implementation code to GitHub.
• Join communities like r/LocalLLaMA or HuggingFace forums.

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Happy building! 💻🧠