# BinRoot/TensorFlow-Book

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

Repository: https://github.com/BinRoot/TensorFlow-Book
Canonical: https://ross.abutalabs.com/products/tensorflow-book
Homepage: http://www.tensorflowbook.com
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: tensorflow, machine-learning, regression, convolutional-neural-networks, logistic-regression, book, reinforcement-learning, autoencoder, linear-regression, classification, clustering
Last push: 2023-03-17T17:59:01+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3832, "days_push": 1265, "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 4427, forks 1177 (observed 2026-08-28T04:08:48.947883+00:00)

## What it is
The official companion source code repository for the book 'Machine Learning with TensorFlow', organized as Jupyter Notebooks per chapter. It provides hands-on examples covering TensorFlow basics through CNNs, RNNs, and reinforcement learning.

## Use cases
- learn machine learning with tensorflow through worked examples
- find example code for linear and logistic regression in tensorflow
- study convolutional neural network implementation notebooks
- get starter code for autoencoders and rnn timeseries prediction
- follow along with a machine learning book chapter by chapter

## When to choose
- you are reading 'Machine Learning with TensorFlow' and want its official code
- you want beginner-friendly notebook-based ML examples
- you need reference implementations of classic ML algorithms in TensorFlow 1.x style

## When to avoid
- you need production-ready or maintained TensorFlow 2.x code
- you want a library or framework rather than educational notebooks
- you need up-to-date deep learning examples with modern tooling

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, tutorials, education
- platform: python
- tags: tensorflow, jupyter-notebooks, book-companion, regression, classification, clustering, reinforcement-learning, cnn, rnn, autoencoder

## Member repositories
- BinRoot/TensorFlow-Book (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:48.947883+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 2026-08-29T18:20:56.019162+00:00, confidence not recorded.
  - readme: https://github.com/BinRoot/TensorFlow-Book (fetched 2026-08-28T04:08:48.947883+00:00, sha 75425ce1b8bd)
- Data as of 2026-08-30T08:39:29.467469+00:00.