# weslynn/AlphaTree-graphic-deep-neural-network

AI Roadmap:机器学习(Machine Learning)、深度学习(Deep Learning)、对抗神经网络(GAN），图神经网络（GNN），NLP，大数据相关的发展路书(roadmap), 并附海量源码（python，pytorch）带大家消化基本知识点，突破面试，完成从新手到合格工程师的跨越，其中深度学习相关论文附有tensorflow caffe官方源码，应用部分含推荐算法和知识图谱

Repository: https://github.com/weslynn/AlphaTree-graphic-deep-neural-network
Canonical: https://ross.abutalabs.com/products/alphatree-graphic-deep-neural-network
License Family: other
Topics: deep-learning, image-classification, machine-learning, neural-network
Last push: 2026-05-11T09:07:36+00:00

## Health v2 (maintenance only)
Score: 69/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 81, release rhythm 35, longevity 100
- inputs: {"age_days": 3102, "days_push": 114, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3013, forks 615 (observed 2026-08-28T04:07:37.843855+00:00)

## What it is
AlphaTree is a curated AI learning roadmap covering machine learning, deep learning, GANs, graph neural networks, NLP, and big data, with visualized model explanations and accompanying Python/PyTorch/TensorFlow source code. It is designed to take learners from beginner to AI application engineer, including interview preparation material.

## Use cases
- learn deep learning from scratch with visual guides
- prepare for machine learning engineer interviews
- understand classic neural network architectures like AlexNet and LeNet
- find annotated source code for deep learning papers
- study GANs and graph neural networks
- get a roadmap for becoming an AI application engineer

## When to choose
- you want a structured, visualized learning path through deep learning fundamentals
- you prefer learning by reading paper summaries alongside reference implementations
- you are preparing for AI engineering interviews

## When to avoid
- you need production-ready deep learning libraries or tools
- you want actively maintained course content rather than a curated reference
- you need English-only materials, as much of the content is in Chinese

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, nlp, data-science
- domain: deep-learning, machine-learning, tutorials, artificial-intelligence
- platform: python
- tags: roadmap, interview-prep, gan, gnn, pytorch, tensorflow, image-classification, chinese-language, awesome-list, natural-language-processing

## Member repositories
- weslynn/AlphaTree-graphic-deep-neural-network (main) score 69

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:37.843855+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 2026-08-30T07:30:11.372979+00:00, confidence not recorded.
  - readme: https://github.com/weslynn/AlphaTree-graphic-deep-neural-network (fetched 2026-08-28T04:07:37.843855+00:00, sha b8bb74ff9120)
- Data as of 2026-08-30T08:39:29.467469+00:00.