Welcome to GenAI-Learnings, a curated hub of resources, projects, and experiments exploring the world of Generative AI. This repository is designed for AI engineers, enthusiasts, and learners who want to explore everything from autonomous agents to NLP, computer vision, vector databases, and practical project implementations.
"Learning AI is fun… but learning Generative AI is like teaching a robot to daydream!" 😄
Explore more of my work:
- Complete Data Science: A deep dive into Data Science AI concepts, tools, and projects and all the material for learning and interview preparation.
- LangChain-Mastery: Everything you need to master LangChain for building powerful LLM applications.
- MCP-YFinance-Server: A backend service for financial analytics and modeling.
- Reinforcement-Learning: Hands-on experiments and theory in Reinforcement Learning.
- CompleteRAG: End-to-end implementation of Retrieval-Augmented Generation (RAG) systems.
- Agentic AI
- Computer Vision
- Data Preprocessing
- Encodings
- HuggingFace
- Vector Databases
- Prompt Engineering
- Quantization
- PlayBook
- Interview Questions
- Small Projects
Hands-on notebooks for autonomous agents using SmolAgents and other frameworks:
Coffee_Ordering_Bot.ipynb– Simple agent that takes coffee ordersFirst Agent.ipynb– Stock market agent using AgnoParty_Agent_smolagents.ipynb– Party-themed agent exampleStock Market Agent.ipynb– Advanced stock market agent
Experiments and tutorials on generative AI applied to images:
AutoEncoders.ipynb– Exploring autoencoders for image reconstructionCNN.ipynb– Convolutional Neural Networks basics and applicationsTransfer_learning.ipynb– Transfer learning with pretrained models
Guides and notebooks for text preprocessing and feature engineering:
Beginner's_Text_Preprocessing.ipynb– Basic text preprocessingText_Classification_ML.ipynb– ML-based text classification preprocessingText_Representation.ipynb– Feature representation for NLP tasks
Tokenization and encoding techniques for NLP:
Byte_Pair_Encoding_tokenization.ipynb– Byte Pair Encoding tutorialTokenizer.ipynb
Fine-tuning, transformer pipelines, and practical NLP projects:
Fine_tuning_masked_model.ipynb– Fine-tune a masked language modelFinetuned_on_AgNews.ipynb– DistilBERT fine-tuned on AgNews datasetFully_trained_bert_on_mrpc.ipynb– BERT-base-uncased on MRPC datasetHuggingFace_Transformers.ipynb– HuggingFace pipeline explorationText_Summarization_Project.ipynb– End-to-end text summarizationText_to_Image_Generation.ipynb` – Basic text-to-image generationTranslation.ipynb– Machine translation experiments
Notebooks for vector database usage and embeddings:
ChromaDB.ipynb– Chroma database experimentsPinecone.ipynb– Pinecone vector DB integration
Notebooks for designing and experimenting with prompts:
COT_Prompting.ipynb– Chain of Thoughts (CoT) promptingFew_Shot_Prompting.ipynb– Few-shot learning with promptsZero_Shot_Prompting.ipynb– Zero-shot learning experiments
Techniques for compressing and optimizing models:
AQLM_Quantization.ipynb– Quantization methodsAWQ_Quantization.ipynb– Advanced quantization experiments
Guides and small projects to apply generative AI concepts:
Anomaly_detection_with_embeddings.ipynb– Embedding-based anomaly detectionBrowser_as_tool_with_LLM.ipynb– Using browser tools with LLMsGemini_Intro.ipynb– Introduction to Gemini AI
Curated questions and answers for LLM and NLP interviews:
100_LLM_INTERVIEW_QUESTIONS.md– 100 essential LLM interview questionsNLP_interview_questions.md– NLP-focused interview questions
- Comprehensive hands-on notebooks covering generative AI concepts and applications
- Includes agent-based AI, computer vision, NLP, vector databases, and small projects
- Focus on practical learning, experimentation, and project-ready skills
- Integrated HuggingFace Transformers, fine-tuning, prompt engineering, and quantization
- Curated interview preparation material and reusable playbooks
- Programming Languages: Python
- Libraries & Frameworks: PyTorch, Transformers, HuggingFace, SmolAgents, FastAPI
- Data & NLP Tools: Pandas, NumPy, NLTK, SpaCy
- Vector Databases: ChromaDB, Pinecone
- Deployment & MLOps: Docker, MLflow, DVC, AWS
Contributions are welcome! Feel free to fork the repository, raise issues, and submit pull requests.