AI Engineering: A Hands-on Guide
A hands-on guide to becoming an AI Engineer, with Jupyter notebooks covering math, PyTorch, deep learning, transformers, RAG, and OCR.
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Core facts
Read by a script from the official API, not rewritten by a model.
- Repository
- https://github.com/Ramakm/AI-Engineering
- License
- MIT
- Languages
- Jupyter Notebook
- Maintainer
- Ramakm
- Homepage
- https://growtechie.substack.com/
- Verified
What it is
The project README describes the repository as a complete, hands-on guide to becoming an AI Engineer. The repository is designed to help you learn AI from first principles, build real neural networks, and understand modern LLM systems end-to-end. The project README says you progress through math, PyTorch, deep learning, transformers, RAG, and OCR, with Jupyter notebooks guiding you at every step.
Main features
The math fundamentals section covers math functions, derivatives, vectors, and gradients, matrix operations and linear algebra, and probability and statistics.
The PyTorch basics section covers creating and manipulating tensors, as well as matrix multiplication, transposing, and reshaping.
The neural network section covers building neurons, layers, and networks from scratch, normalization techniques (RMSNorm), and optimizers (Adam, Muon) and learning rate decay.
The transformers section covers attention and self-attention mechanisms, multi-head attention, and decoder-only transformer architecture.
The RAG section covers building RAG pipelines end to end, indexing, retrieval, and chunking strategies, and integrations with embedding models and vector stores.
The OCR section covers an OCR pipeline and utilities, as well as preprocessing images and extracting text.
Getting started
The Requirements section says to install dependencies.
The usage steps say to open Jupyter in the project root.
The usage steps also say to work through notebooks in order.
The folder guide lists a learning path for going through the repo in order.
The folder guide lists a FAISS-based RAG pipeline with pluggable LLM providers and tests as a project.
The folder guide also lists an OCR service with preprocessing utilities and Render deployment as a project.
pip install -r requirements.txt jupyter lab
# or
jupyter notebookWho it is for
The recommended reading books are not included in the repository. YoutubeMCP requires Python 3.12 or higher and uv. The Fine-Tuning folder contains LoRA, QLoRA, and Unsloth fine-tuning notebooks.
The YoutubeMCP folder contains a minimal MCP server for fetching YouTube transcripts. The Brazil-Ecommerce-Data-Analysis folder contains exploratory data analysis on the Olist dataset. The DL folder contains deep learning projects with production-style serving (FastAPI / Streamlit).
The project is licensed under the MIT License. The project welcomes contributions.
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Sources
This page is written from the official sources below. Open them to check the original text.
- GitHub READMEhttps://github.com/Ramakm/AI-EngineeringFetched
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