SkalskiP/vlms-zero-to-hero resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1179 stars · 104 forks observed · 2026-08-28

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

A tutorial series of Jupyter notebooks teaching the path from NLP and computer vision fundamentals to Vision-Language Models. Each notebook implements classic models like Word2Vec, BERT, and CLIP in PyTorch with links to the original papers.

Use cases

• learn how vision-language models work from scratch
• implement word2vec in pytorch
• understand transformers and attention from papers
• study clip and multimodal embeddings
• hands-on deep learning fundamentals course
• learn lora fine-tuning

When to choose

• you want paper-to-code implementations of foundational NLP and CV models
• you learn best from runnable notebooks with Colab support
• you want a structured path toward understanding VLMs

When to avoid

• you need production-ready model code or a library
• you want a polished finished course - the series is still being released
• you need non-PyTorch frameworks like TensorFlow or JAX

Facets

learning-resource · maturity active

machine-learning deep-learning nlp computer-vision llm-training deep-learning computer-vision tutorials artificial-intelligence python cross-platform vision-language-models jupyter-notebooks tutorials pytorch transformers word2vec clip lora educational-series natural-language-processing

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