AI Engineering: Hands-on

A complete, hands-on guide to becoming an AI Engineer.

This repository is designed to help you learn AI from first principles, build real neural networks, and understand modern LLM systems end-to-end. You'll progress through math, PyTorch, deep learning, transformers, RAG, and OCR — with clean, intuitive Jupyter notebooks guiding you at every step.

Whether you're a beginner or an engineer levelling up, this repo gives you the clarity, structure, and intuition needed to build real AI systems.

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Repository Structure

1. Math Fundamentals

• Math functions, derivatives, vectors, and gradients
• Matrix operations and linear algebra
• Probability and statistics

2. PyTorch Basics

• Creating and manipulating tensors
• Matrix multiplication, transposing, and reshaping
• Indexing, slicing, and concatenating tensors
• Special tensor creation functions

3. Neural-Network(NN)

• Building neurons, layers, and networks from scratch
• Normalization techniques (RMSNorm)
• Activation functions
• Optimizers (Adam, Muon) and learning rate decay

4. Transformers

• Attention and self-attention mechanisms
• Multi-head attention
• Decoder-only transformer architecture

5. Retrieval-Augmented Generation (RAG)

• Building RAG pipelines end to end
• Indexing, retrieval, chunking strategies
• Integrations with embedding models and vector stores
• Cloud LLM support: Atlas Cloud (deepseek-ai/DeepSeek-V3-0324 by default), MiniMax (M3), OpenAI, or any OpenAI-compatible API for richer answer generation

6. Optical Character Recognition (OCR)

• OCR pipeline and utilities
• Preprocessing images and extracting text

7. Additional Modules

Beyond the numbered learning track, the repository also contains the following folders:

• LLM: LLM fundamentals, tokens, and token embeddings
• Fine-Tuning: LoRA, QLoRA, and Unsloth fine-tuning notebooks
• Basic ML Model Implementation: Classical supervised and unsupervised ML notebooks
• DL: Deep learning projects with production-style serving (FastAPI / Streamlit)
• Brazil-Ecommerce-Data-Analysis: Exploratory data analysis on the Olist dataset
• YoutubeMCP: A minimal MCP server for fetching YouTube transcripts
• Interview Prep, Books, Data, Start_here: Supporting resources and sample data

Project Structure

A detailed map of the repository:

ai-hands-on/
├── README.md                         # This file
├── LICENSE                           # MIT License
├── requirements.txt                  # Root Python dependencies
├── ANN_&_CNN.ipynb                   # ANN vs CNN on Fashion-MNIST (root copy)
├── ATLAS_CLOUD_PROVIDER_REVIEW.md    # Notes on the Atlas Cloud provider integration in 5.RAG
├── test.txt                          # Sample network-intrusion CSV data
│
├── .github/
│   └── FUNDING.yml                   # Sponsorship config
│
├── Start_here/
│   └── learning_path.md              # Recommended step-by-step learning path
│
├── 0.1-Do-you-know/                  # Short "did you know" explainers
│   ├── ANN(Artificial_Neural_Network_).ipynb
│   └── Efficient_Iteration_In_Pandas.ipynb
│
├── 1.Math/                           # Math fundamentals
│   ├── 1. Math Functions.ipynb
│   ├── 2. Partial Derivatives.ipynb
│   ├── 3. Vectors.ipynb
│   ├── 4. Gradient.ipynb
│   ├── 5. Metrics.ipynb
│   └── 6. Probability.ipynb
│
├── 2.Pytorch/                        # PyTorch tensor basics
│   ├── 1. Tensor.ipynb
│   ├── 2. Matrix Multiplication.ipynb
│   ├── 3. Tensor Transposing.ipynb
│   ├── 4. Reshaping tensor.ipynb
│   ├── 5. Indexing & Slicing.ipynb
│   ├── 6. Tensor Concatenating.ipynb
│   └── 7. Linspace and Arrange Tensor.ipynb
│
├── 3.Neural-Network(NN)/             # Neural networks from scratch
│   ├── 1.Single Neuron.ipynb
│   ├── 2. Layer building.ipynb
│   └── 3. BackPropagation.md
│
├── 4.Transformer/                    # Transformer building blocks
│   ├── 1. Attention Mechanism.ipynb
│   ├── 2. Self Attention.ipynb
│   ├── 3. Multi-head Attention.ipynb
│   └── 4. Decoder.ipynb
│
├── 5.RAG/                            # Retrieval-Augmented Generation (cybersecurity use case)
│   ├── README.md
│   ├── .env.example                  # API key / provider configuration template
│   ├── requirements.txt
│   ├── data/processed_texts/         # Source documents (firewall, incident response, vulnerability scan)
│   ├── embeddings/faiss_index/       # Prebuilt FAISS index + metadata
│   ├── src/
│   │   ├── extract_text.py           # Text extraction
│   │   ├── create_embeddings.py      # Build embeddings and FAISS index
│   │   ├── retrieve_context.py       # Semantic retrieval
│   │   ├── generate_answer.py        # Answer generation
│   │   ├── llm_provider.py           # Provider abstraction (Atlas Cloud, MiniMax, OpenAI, compatible APIs)
│   │   └── app.py                    # Application entry point
│   └── tests/                        # test_integration.py, test_llm_provider.py
│
├── 6.OCR/                            # Optical Character Recognition
│   ├── README.md
│   ├── main.py                       # API / app entry point
│   ├── utils.py                      # Image preprocessing and OCR helpers
│   ├── pytess_gemini.ipynb           # Pytesseract + Gemini notebook
│   ├── render.yaml                   # Render deployment config
│   ├── requirements.txt
│   └── sample_data/                  # Sample images (incl. a rotated one)
│
├── LLM/                              # LLM fundamentals
│   ├── 1_LLM_Fundamentals_.ipynb
│   └── 2_Tokens_and_Token_Embeddings.ipynb
│
├── Fine-Tuning/                      # Parameter-efficient fine-tuning
│   ├── Fine_Tuning_a_JSON_File_Using_Unsloth_&_LoRA.ipynb
│   ├── Lora-Implementation.ipynb
│   └── Qlora-Implementation.ipynb
│
├── Basic ML Model Implementation/    # Classical ML notebooks
│   ├── 1_Base_ML_Model_Linear_&_Ridge_Regression.ipynb
│   ├── LM_Implementation.ipynb
│   ├── Logistic_Regression_Model_Implementation.ipynb
│   ├── Decision_Tree_Model_Implementation.ipynb
│   ├── Naïve_Bayes_Classification_Implementation_.ipynb
│   ├── Support_Vector_Machine_[SVM]_Classification_Implementation_.ipynb
│   ├── Classification_Models_Implementation[Supervised].ipynb
│   └── Unsupervised_Learning_Models_Implementation.ipynb
│
├── DL/                               # Deep learning projects (notebook -> production)
│   ├── ANN-CNN Prod/                 # Fashion-MNIST ANN vs CNN served with FastAPI
│   │   ├── train.py, config.py
│   │   ├── app/                      # FastAPI app, preprocessing, static upload page
│   │   └── models/                   # Trained .keras models + metrics.json
│   ├── RNN-LSTM Prod/                # NumPy RNN vs LSTM from scratch + FastAPI + web UI
│   │   ├── seqnet/                   # Models, optimizers, training, persistence, API
│   │   ├── web/                      # Frontend (HTML/CSS/JS)
│   │   ├── artifacts/                # Trained weights
│   │   ├── tests/                    # Gradient checks, API, notebook sync tests
│   │   └── Dockerfile, Makefile, start.sh
│   └── RNN-LSTM-GRU/                 # HAR sensor data: RNN vs LSTM vs GRU
│       ├── backend/                  # Config, data, model, training, FastAPI
│       ├── frontend/app.py           # Streamlit UI
│       └── artifacts/                # Trained models, scaler, encoder, results
│
├── Brazil-Ecommerce-Data-Analysis/   # Olist e-commerce EDA
│   ├── README.md
│   └── customer / geolocation / order_items / order_payments /
│       order_reviews / orders / products / sellers _analysis.ipynb
│
├── YoutubeMCP/                       # MCP server for YouTube transcripts
│   ├── README.md, Project.md
│   ├── main.py, server.py, test.py
│   ├── pyproject.toml
│   └── src/                          # service.py, utils.py
│
├── Data/                             # Sample datasets
│   ├── json_extraction_dataset_500.json   # Used for JSON-extraction fine-tuning
│   └── test.txt
│
├── Interview Prep/
│   └── AI Engineering Topics To Prepare Must.html
│
└── Books/                            # Reference PDFs (SQL, AI Engineering, Deep Learning, ML)

Folder Guide

Books

Recommended reading to deepen your understanding (not included):

• AI Engineering by Chip Huyen
• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
• The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• Neural Networks and Deep Learning by Michael Nielsen
• SQL Cookbook by Anthony Molinaro

For more books in AI/ML, I have created another repo for this Check Here. I will be adding lot more in coming days/months. If you are interested to read book, go check this repo out.

Learning Path

For a recommended step-by-step progression through the materials, see the Learning Path:

• Start_here/learning_path.md

Requirements

Install dependencies with:

pip install -r requirements.txt

Some subfolders (for example 5.RAG/ and 6.OCR/) include their own requirements.txt with additional dependencies.

Usage

Recommended workflow:

1. Open Jupyter in the project root:

   jupyter lab
   # or
   jupyter notebook
   

2. Work through notebooks in order:

   • 1.Math/
   • 2.PyTorch/
   • 3.Neural-Network(NN)/
   • 4.Transformer/

3. Folder to run separately:

   • 5.RAG/
   • 6.OCR/
   • DL/ANN-CNN Prod/, DL/RNN-LSTM Prod/, DL/RNN-LSTM-GRU/ (each has its own README and requirements)
   • YoutubeMCP/ (requires Python 3.12+ and uv)

4. Resources

5. Basic ML Model Implementation (Supervised + Un-supervised + RL)

   • 1.Linear Regression
   • 2.Logistic Regression
   • 3.Decision Tree Model
   • 4.Naive Bayes Classification

Machine Learning Frameworks

GEN AI References

Contributing

Contributions are welcome!

Please ensure:

• Notebooks are clean (Restart & Run All before committing)
• Existing structure & naming conventions are followed
• PRs are focused, readable, and documented
• In folders like RAG and OCR, please maintain the cleaned structure part
• If you want to add something new folders, make it proper structure way.

License

• This project is licensed under the MIT License. See LICENSE for details.

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