# TrickyGo/Dive-into-DL-TensorFlow2.0

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为TensorFlow 2.0实现，项目已得到李沐老师的认可

Repository: https://github.com/TrickyGo/Dive-into-DL-TensorFlow2.0
Canonical: https://ross.abutalabs.com/products/dive-into-dl-tensorflow20
Homepage: https://trickygo.github.io/Dive-into-DL-TensorFlow2.0/#/
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: deep-learning, python3, dive-into-deep-learning, tensorflow2, jupyter-notebook, nlp, cv, tutorials, chinese-simplified, book
Last push: 2023-03-17T08:51:52+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 3054, "days_push": 1265, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_readme
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3823, forks 814 (observed 2026-08-28T04:08:21.988227+00:00)

## What it is
A Chinese-language open-source book that ports the 'Dive into Deep Learning' (D2L) textbook's MXNet code examples to TensorFlow 2.0, presented as Jupyter notebooks. It is endorsed by the original author Mu Li and covers deep learning fundamentals through NLP and computer vision.

## Use cases
- learn deep learning with tensorflow 2.0
- study dive into deep learning book in chinese
- hands-on deep learning tutorials with jupyter notebooks
- learn neural networks from scratch with code examples
- study nlp and computer vision fundamentals
- convert d2l mxnet examples to tensorflow

## When to choose
- you want to learn deep learning theory alongside runnable TensorFlow 2.0 code
- you prefer Chinese-language instructional material
- you are following the Dive into Deep Learning curriculum but use TensorFlow instead of MXNet

## When to avoid
- you need PyTorch or MXNet implementations
- you want up-to-date TensorFlow 2.x best practices, as the project is in maintenance mode
- you need production-grade deep learning code rather than educational examples

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: deep-learning, machine-learning, nlp, computer-vision
- domain: deep-learning, machine-learning, tutorials, computer-vision
- platform: python, cross-platform
- tags: tensorflow2, jupyter-notebook, dive-into-deep-learning, chinese, textbook, mxnet-port, natural-language-processing

## Member repositories
- TrickyGo/Dive-into-DL-TensorFlow2.0 (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:21.988227+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 2026-08-29T18:26:23.557624+00:00, confidence not recorded.
  - homepage: https://trickygo.github.io/Dive-into-DL-TensorFlow2.0/#/ (fetched 2026-08-29T09:21:14.914102+00:00, sha 134d2f236d77)
- Data as of 2026-08-30T08:39:29.467469+00:00.