# SkalskiP/vlms-zero-to-hero

This series will take you on a journey from the fundamentals of NLP and Computer Vision to the cutting edge of Vision-Language Models.

Repository: https://github.com/SkalskiP/vlms-zero-to-hero
Canonical: https://ross.abutalabs.com/products/vlms-zero-to-hero
Homepage: https://www.youtube.com/@SkalskiP
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: bert-model, clip, computer-vision, embeddings, gpt, gpt-2, lora, natural-language-processing, seq2seq, vision-language-model, word2vec
Last push: 2025-01-23T11:23:09+00:00

## Health v2 (maintenance only)
Score: 22/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 3, release rhythm 35, longevity 44
- inputs: {"age_days": 621, "days_push": 587, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1179, forks 104 (observed 2026-08-28T04:03:53.233684+00:00)

## What it is
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
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, nlp, computer-vision, llm-training
- domain: deep-learning, computer-vision, tutorials, artificial-intelligence
- platform: python, cross-platform
- tags: vision-language-models, jupyter-notebooks, tutorials, pytorch, transformers, word2vec, clip, lora, educational-series, natural-language-processing

## Member repositories
- SkalskiP/vlms-zero-to-hero (main) score 22

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:53.233684+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:25:30.230863+00:00, confidence not recorded.
  - readme: https://github.com/SkalskiP/vlms-zero-to-hero (fetched 2026-08-28T04:03:53.233684+00:00, sha 10862bc89670)
  - homepage: https://www.youtube.com/@SkalskiP (fetched 2026-08-29T12:32:37.552298+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.