RAG Frameworks · Ramakm

ai-hands-on

ai-hands-on is an educational repository of Jupyter notebooks and resources teaching AI fundamentals from scratch, covering math, PyTorch, neural networks, transformers, RAG, and OCR. It is designed as a structured learning path for beginners and practitioners, with no released versions or production-grade tooling.

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Source: GitHub — github.com/Ramakm/ai-hands-on

1.3k

GitHub stars

280

Forks

Jupyter Notebook

Primary language

MIT

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	Ramakm/ai-hands-on
Owner	Ramakm
Primary language	Jupyter Notebook
License	MIT — OSI-approved
Stars	1.3k
Forks	280
Open issues	0
Latest release	Unknown
Last updated	2026-06-21
Source	https://github.com/Ramakm/ai-hands-on

What ai-hands-on is

A collection of pedagogical Jupyter notebooks implementing core ML/AI concepts: linear algebra and calculus foundations, PyTorch tensor operations, custom neural network layers, transformer architectures with attention, RAG pipelines (with Atlas Cloud, MiniMax, OpenAI API support), and OCR utilities. Built on MIT license; no formal versioning or API surface.

Quickstart

Get the ai-hands-on source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/Ramakm/ai-hands-on.git2cd ai-hands-on3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

Self-directed AI/ML learning path

Teams or individuals seeking a structured, hands-on curriculum from math fundamentals through transformer architectures and RAG. Notebooks provide worked examples and intuition-building exercises.

Engineering onboarding for AI/ML roles

Use as a reference or companion to train junior engineers on core concepts: gradient descent, backpropagation, attention mechanisms, and modern LLM workflows before engaging production systems.

Rapid prototyping of RAG and OCR workflows

Provided RAG and OCR modules offer templates for indexing, retrieval, and text extraction. Integrations with public LLM APIs (OpenAI, Atlas Cloud, MiniMax) reduce setup friction for small-scale experimentation.

Implementation considerations

Requires Jupyter Lab/Notebook runtime and Python environment management. Install dependencies from root or per-folder requirements.txt files; some modules (RAG, OCR) have additional constraints.
External API keys needed for RAG workflows: Atlas Cloud, MiniMax, or OpenAI. Manage secrets and rate limits appropriately; no built-in credential management.
Notebooks are linear guides; adapting them for team collaboration or CI/CD requires converting to Python modules, adding error handling, and structuring for reuse.
Math and PyTorch modules assume intermediate Python; OCR and RAG modules add external library dependencies (vector stores, embedding models) that must be installed separately.
No formal testing, versioning, or deprecation policy. Updates to the repo may break imports or assumptions; pin dependency versions in production use.

When to avoid it — and what to weigh

Production AI/ML deployment required — This is a learning resource, not an optimized framework. No production hardening, performance guarantees, error handling patterns, or observability tooling. Use established libraries (Hugging Face, PyTorch Lightning, LangChain) for production workloads.
Enterprise-grade support or SLAs needed — Single-author, educational repository. No formal support channels, security vulnerability response process, or maintenance roadmap. Requires internal capability to debug and adapt.
Commercial AI service or product dependency — RAG examples depend on external LLM APIs (Atlas Cloud, MiniMax, OpenAI). Licensing, cost, and availability of these services are not under control and may be unsuitable for regulated/isolated environments.
Non-English-speaking learners seeking native-language materials — All content is in English. No translations or multilingual resources provided.

License & commercial use

MIT License (permissive OSI-compliant license). Permits use, modification, and redistribution with minimal restrictions; requires retention of license and copyright notice in derivatives.

MIT License permits commercial use. However, reliance on external services (Atlas Cloud, MiniMax, OpenAI APIs) introduces separate commercial terms. Review those vendors' commercial use policies and licensing separately. No indemnification or warranty from this repository.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Good
Assessment confidence	High

Security considerations

Educational repository; no explicit security audit or hardening. Risk areas: (1) External API keys in notebooks or config files—manage secrets via environment variables or secure vaults before production use. (2) RAG relies on external LLM services; apply the security posture of those vendors (Atlas Cloud, MiniMax, OpenAI). (3) OCR preprocessing may expose sensitive image data; implement appropriate data handling and retention policies. No encryption, authentication, or access control mechanisms built-in.

Alternatives to consider

Hugging Face Course / Transformers Library

Official, production-ready framework with extensive docs, community support, and versioning. Better for production deployment; steeper initial curve for fundamentals.

Fast.ai (Practical Deep Learning for Coders)

Comprehensive video + notebook curriculum with strong emphasis on intuition and practitioner focus. Larger community and curated progression; less granular control over underlying math.

DeepLearning.AI short courses (LLMs, RAG, etc.)

Structured, professionally produced lessons covering transformers, fine-tuning, and RAG. Shorter time-to-competency for specific topics; not a full AI engineering path and may require paid certificates.

Software development agency

Build on ai-hands-on with DEV.co software developers

Clone the repository and follow the learning path in Start_here/learning_path.md. Work through notebooks sequentially: Math → PyTorch → Neural Networks → Transformers → RAG & OCR.

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ai-hands-on FAQ

Can I use this for production AI services?

Not directly. This is a learning resource. Extract concepts and logic, then implement using production-ready libraries (PyTorch Lightning, Hugging Face, LangChain). Add error handling, monitoring, and security hardening.

What are the system requirements?

Python 3.x with Jupyter Lab or Notebook, plus dependencies listed in requirements.txt files. RAG and OCR modules have additional dependencies (vector stores, embedding models, image libraries). GPU recommended for larger neural network examples.

Is there an official certification or completion badge?

Unknown. Repository does not mention certificates. This is an open-source learning guide, not a formal course platform.

What if I find a bug or have questions?

File an issue on GitHub. Responses depend on maintainer availability; no SLA. Community contributions are welcome but review latency is uncertain.

Software developers & web developers for hire

DEV.co helps companies turn open-source tools like ai-hands-on into production software. Our software development services cover the full lifecycle — architecture, web development, integration, and maintenance — delivered by software developers and web developers who ship. Engage our software development agency to implement or customize it for your rag frameworks stack.

Start Your AI Engineering Journey

Clone the repository and follow the learning path in Start_here/learning_path.md. Work through notebooks sequentially: Math → PyTorch → Neural Networks → Transformers → RAG & OCR.

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