# xiaohu2015/DeepLearning_tutorials

The deeplearning algorithms implemented by tensorflow

Repository: https://github.com/xiaohu2015/DeepLearning_tutorials
Canonical: https://ross.abutalabs.com/products/deeplearning_tutorials
Language: Jupyter Notebook
License Family: other
Topics: tensorflow, deep-learning-algorithms, cnn, dbn, deep-learning-tutorial, lstm, sda, mlp, rbm
Last push: 2019-02-27T02:31:07+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3548, "days_push": 2745, "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 1841, forks 759 (observed 2026-08-28T04:05:43.133619+00:00)

## What it is
A collection of deep learning algorithms (MLP, CNN, autoencoders, RBM, DBN, and modern CNN architectures) implemented from scratch in TensorFlow for educational purposes, modeled after the classic Theano Deep Learning Tutorials. It is a tutorial codebase of Jupyter notebooks and Python scripts rather than a production library.

## Use cases
- learn how classic deep learning models are implemented in tensorflow
- study implementations of rbm and deep belief networks
- understand cnn architectures like mobilenet and densenet from scratch
- tutorial code for logistic regression and mlp in tensorflow
- reference implementations of yolov1 object detection

## When to choose
- you want readable, from-scratch implementations of classic deep learning models for study
- you are learning fundamentals of tensorflow 1.x and neural network basics

## When to avoid
- you need a maintained library for production deep learning
- you use modern tensorflow 2.x or pytorch versions
- you need supported, tested model implementations

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: deep-learning, machine-learning
- domain: deep-learning, machine-learning, tutorials
- platform: python
- tags: tensorflow, jupyter-notebook, educational, cnn, autoencoder, rbm, object-detection

## Member repositories
- xiaohu2015/DeepLearning_tutorials (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:43.133619+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 2026-08-30T03:18:01.681931+00:00, confidence not recorded.
  - readme: https://github.com/xiaohu2015/DeepLearning_tutorials (fetched 2026-08-28T04:05:43.133619+00:00, sha 0c5c5f98ce3f)
- Data as of 2026-08-30T08:39:29.467469+00:00.