🤖 Gen AI Learning Journey – From Basics to Transformers

Welcome to my personal repository for mastering Generative AI – a curated collection of notes, code snippets, and hands-on experiments. This repo is structured to help me (and others) understand the entire Gen-AI stack from the ground up.

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🧠 Topics Covered

📌 Foundations

• ✅ Python Basics (OOP, NumPy, Pandas, etc.)
• ✅ Mathematics for ML – Linear Algebra, Probability, Calculus
• ✅ ML Algorithms – Regression, Classification, Clustering, etc.
• ✅ Deep Learning – ANN, CNN, RNN, LSTM

🗣️ Natural Language Processing (NLP)

• ✅ Text Preprocessing (Tokenization, Stemming, Lemmatization)
• ✅ Word Embeddings – Word2Vec, GloVe, FastText
• ✅ Sequence Models – LSTM, GRU
• ✅ Attention Mechanism
• ✅ Transformers (Self-Attention, Encoder-Decoder)
• ✅ BERT, GPT, T5 – Concept + Hands-On

🔗 LangChain & Gen AI

• ✅ LangChain Basics
• ✅ Prompt Engineering
• ✅ Chains & Agents
• ✅ Integrating OpenAI APIs
• ✅ Building LLM-powered apps
• ✅ Retrieval Augmented Generation (RAG)
• ✅ LangChain + Vector DBs (FAISS, Chroma, etc.)

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🚀 Why This Repo?

I created this as a centralized knowledge base to:

• Strengthen my understanding of GenAI fundamentals
• Document hands-on implementation of concepts
• Help others get started with Generative AI step by step

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🗂️ Folder Structure

gen-ai-learning/
├── python-basics/
├── ml-algorithms/
├── deep-learning/
├── nlp/
├── transformers/
├── langchain/
├── projects/
└── README.md