AI, ML, Deep Learning & LLMs — Content & Cookbooks

A curated, hands-on library of notebooks, demos, and resources for AI/ML, Deep Learning, Generative AI, RAG, agents, fine-tuning, and modern tooling.

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📦 PyPI Packages

InstructVault

• PyPI: https://pypi.org/project/instructvault/
• GitHub: https://github.com/05satyam/instruct_vault

pip install instructvault

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Non-Convex Optimization Benchmark Functions

• PyPI: https://pypi.org/project/nonconvexoptimzationfunclib/
• GitHub: https://github.com/05satyam/nonconvexoptimizationfunclib

A Python library of standard non-convex benchmark functions for evaluating and comparing optimization algorithms.

pip install nonconvexoptimzationfunclib

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📌 Table of Contents

1. Mission & Scope
2. Pick your path
3. Foundations (AI/ML Core)
4. LangChain (All Notebooks)
5. LlamaIndex (All Notebooks)
6. RAG Systems (All Variants)
7. Agents & Orchestration
8. Graph & Multimodal
9. MCP (Model Context Protocol)
10. Observability
11. Interview Experiences
12. External Resources
13. Utils
14. Repository Rules
15. Contributing & Support
16. License & Citation

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🎯 Mission & Scope

This repository is a living library of practical AI/ML and Generative AI knowledge.
The focus is on learning by doing — notebooks and guides are reproducible, intuitive, and easy to extend.

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✨ Pick your path

If you're new, start with the path that matches your goal such as:

flowchart LR
  %% ========== Styles ==========
  classDef start fill:#E8F5E9,stroke:#2E7D32,stroke-width:1px,color:#1B5E20;
  classDef step  fill:#E3F2FD,stroke:#1565C0,stroke-width:1px,color:#0D47A1;
  classDef choice fill:#FFF8E1,stroke:#F57F17,stroke-width:1px,color:#E65100;
  classDef path  fill:#F3E5F5,stroke:#6A1B9A,stroke-width:1px,color:#4A148C;
  classDef build fill:#FFEBEE,stroke:#C62828,stroke-width:1px,color:#B71C1C;

  %% ========== Flow ==========
  A["🚀 Start Here<br/>Clone repo + setup venv"]:::start
  C{"Pick your learning path"}:::choice

  subgraph P["✨ Learning Paths"]
    direction TB
    F["🧠 Foundations<br/>Tokens • Embeddings • Transformers"]:::path
    H["🛠️ Hands-on Notebooks<br/>Run examples first"]:::path
    R["🔎 RAG Systems<br/>Naive → Hybrid → Graph → Multimodal"]:::path
    AG["🤖 Agents<br/>ReAct • Plan&Execute • Reflexion"]:::path
    I["🎯 Interview Prep<br/>System design + Q&A"]:::path
  end

  D["🔍 Observability & Evaluation<br/>Tracing • quality checks"]:::step
  E["🏗️ Build Mini-Projects<br/>End-to-end demo apps"]:::build
  Z["🌟 Keep iterating<br/>Add your own examples + PRs"]:::start

  %% ========== Edges ==========
  A --> C
  C --> F
  C --> H
  C --> R
  C --> AG
  C --> I

  F --> H
  H --> AG
  R --> AG
  AG --> D --> E --> Z

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Foundations (AI/ML Core)

Notebook	What you’ll learn	Level
Tokens in GenAI	Tokenization intuition + cost/latency impact	Beginner
ML Word Embeddings	Word2Vec/GloVe/CBOW intuition	Beginner
Simple LoRA Fine-Tuning	PEFT/LoRA fine-tuning end-to-end	All Levels
Best Practices for Building AI Agents (Framework-Agnostic)	Best Practices	All Levels

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🔗 LangChain (All Notebooks)

Everything that uses LangChain / LangGraph / LCEL lives here.

Notebook	What it does	Level	Tags
LangChain Prompt Chains	Prompt chaining + LCEL patterns	All Levels	#prompting #lcel
Plan & Execute (LangGraph)	Multi-step planning + execution	All Levels	#langgraph #agents
Reflexion Pattern	Self-critique agent loops	All Levels	#agents #reasoning
LangGraph Agents	Tool-calling agents with graphs	All Levels	#langgraph #tools

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🦙 LlamaIndex (All Notebooks)

Everything that uses LlamaIndex lives here.

Notebook	What it does	Level	Tags
Text-to-SQL w/ LlamaIndex	Natural language → SQL over DB	All Levels	text2sql, llamaindex
LlamaExtract (LlamaIndex)	Structured extraction from invoices using LlamaIndex	All Levels	llamaindex, extraction

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🔎 RAG Systems (All Variants)

All Retrieval-Augmented Generation notebooks, grouped by type.

Notebook	RAG Type	What it does	Level
Hybrid Search RAG	Hybrid RAG	BM25 + vectors + reranking	All Levels
Semantic Search (Pinecone)	Vector RAG	Simple embedding retrieval	All Levels
GraphRAG	Graph RAG	Graph retrieval + LLM answering	Advanced
Multimodal RAG: Text + Images	Multimodal RAG	Retrieve across text & images	All Levels

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🤖 Agents & Orchestration

Notebook / Resource	What it does	Level
LLM Query Router	Route queries to best chain/tool	All Levels
PydanticAI Agents And Tools	Typed agents + strict tool schemas	All Levels
PydanticAI Agentic Lib	Agentic patterns using PydanticAI	All Levels
Crew AI Agents	Multi-agent teams + roles	All Levels
Agentic Webcrawler Chatbot	Crawl web + answer with agents	All Levels
Agentic Design Patter Cookbook	Agentic Design Pattern cookbook — ReAct, Plan & Execute, Self Check using Langchain	All levels

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🕸️ Graph & Multimodal

Notebook	What it does	Level
GraphMyDoc	Build doc knowledge graphs	All Levels
GraphNavAI	Navigate knowledge as graph	All Levels

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🧩 MCP (Model Context Protocol)

Demo	What it does	Level
Hello World — MCP	Hello World — MCP	All Levels
MCP Server Demo	MCP server-client tooling end-to-end	All Levels

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📈 Observability

Notebook	What it does	Level
LlamaTrace — Observability	Tracing, evals, monitoring with Phoenix	All Levels
ARIZE — Observability	AI-AGENT Tracing, evals, monitoring with ARIZE	All Levels

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🎯 Interview Experiences

Doc	Focus Area
LLM Architecture Comparison	Evolution of LLM architectures (2017–2025)
Interview Q&A	Common AI/ML/LLM interview questions
Contextual & GPT Embeddings	Embedding types + intuition
AI Agent Memory Types	Memory patterns for agents
Stanford LLM Cheatsheet	Compact transformer/LLM summary

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📚 External Resources

Free Open Source Learning Resources

🧠 Provider	📚 Resource	🔍 Focus Area
OpenSource Book: Agentic Design Patterns	Agentic Design Patterns	Hands-on agentic systems
LangChain	Chat LangChain	Chat with LangChain docs
	LangChain for LLM App Dev	Prompting, chains, memory
	Functions, Tools & Agents	Tool calling, agents
	LangGraph Intro Course	Agentic execution
	LangChain Tutorials	End-to-end apps
LlamaIndex	Chat LlamaIndex	Chat with LlamaIndex docs
	Advanced RAG Certification	Production RAG
	Agentic RAG Course	Agentic RAG
	LlamaIndex Docs	Indexing & ingestion
Hugging Face	LLM Course	Transformers & tokenizers
	AI Agents Course	Agent architectures
	Diffusion Models Course	Image diffusion
	Open Source Models	Discovery & eval
Microsoft	Generative AI for Beginners	GenAI foundations
	AI for Beginners	Classical AI/ML
	AI Agents for Beginners	Agent systems
AWS	Intro to GenAI	Enterprise GenAI
	Prompt Engineering Essentials	Prompting
	Responsible AI	Governance
	AWS PartyRock	No-code GenAI apps
Meta (LLaMA)	Building with Llama 4	Llama models

AI & ML Tools

• LLM Visualization
• Chatbot Arena LLM Leaderboard

Technical Blogs

• DeepLearning.AI — The Batch
• Uber AI — LLM Training
• Netflix ML Recommendations
• Sebastian Raschka

Industry AI & ML Talks

• Deep Dive into LLMs — Andrej Karpathy
• Deep Dive into LLMs like ChatGPT
• Software Is Changing (Again)
• Making AI accessible
• What is Agentic AI?
• Discover AI — Code4AI
• The AI GRID
• Krish Naik

Technical Newsletters

• AI by Hand — Dr. Tom Yeh
• LLM Watch — Pascal Biese
• LangChain Blog
• LlamaIndex Blog
• MLOps Architect Mindset
• ByteByteGo Newsletter

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🧰 Utils

• script_to_update_packages.py
• Steps2CreateEnvFile.MD
• requirements.txt

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📜 Repository Rules

• Contributing Guide
• Code of Conduct
• Security Policy
• MIT License
• Citation File

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🤝 Contributing & Support

Contributions are welcome!
If you spot an error, want a new notebook, or have an improvement idea:

• Read the Contributing Guide
• Open a PR / issue with a clear description

Security issues should be reported privately (see SECURITY.md).

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📜 License

This project is licensed under the MIT License — see the LICENSE file for details.

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⭐ Final Note

These notebooks reflect personal learnings and experiments.
Mistakes are part of the journey — use this repo as a starting point and adapt freely.

If this helps you, consider giving it a ⭐ on GitHub — it helps others find it too.