Ramakm/ai-hands-on resource

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1432 stars · 301 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A collection of Jupyter notebooks and supporting files that teach AI engineering from first principles, covering math, PyTorch, neural networks, transformers, RAG, and OCR. It is a structured, hands-on curriculum for beginners and engineers levelling up in AI.

Use cases

• learn AI from scratch with guided notebooks
• build neural networks from first principles in PyTorch
• understand transformer and attention mechanisms
• build an end-to-end RAG pipeline with embeddings and vector stores
• learn OCR image preprocessing and text extraction
• review math fundamentals like linear algebra and gradients for ML
• follow a structured path to become an AI engineer

When to choose

• you want a free, notebook-driven curriculum covering math through LLM systems
• you prefer learning by building neural networks and RAG pipelines from scratch
• you are a beginner or engineer transitioning into AI engineering

When to avoid

• you need production-ready AI libraries or frameworks rather than learning material
• you want a comprehensive course with graded exercises and certification
• you need non-PyTorch frameworks like TensorFlow or JAX examples

Facets

learning-resource · maturity active

machine-learning deep-learning rag ocr nlp artificial-intelligence machine-learning deep-learning large-language-models education tutorials python cross-platform jupyter-notebooks pytorch transformers neural-networks math-fundamentals hands-on-learning ai-engineering

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markdown · JSON · MCP: product_card(name="Ramakm/ai-hands-on")