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https://github.com/nanxstats/deep-learning-recipes

🍱 R implementation for selected machine learning methods with deep learning frameworks (Keras, Tensorflow)
https://github.com/nanxstats/deep-learning-recipes

deep-learning keras matrix-factorization r recommender-system tensorflow triplet-loss

Last synced: about 2 months ago
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🍱 R implementation for selected machine learning methods with deep learning frameworks (Keras, Tensorflow)

Host: GitHub
URL: https://github.com/nanxstats/deep-learning-recipes
Owner: nanxstats
License: mit
Created: 2018-08-25T06:05:18.000Z (about 6 years ago)
Default Branch: master
Last Pushed: 2019-03-18T02:10:50.000Z (over 5 years ago)
Last Synced: 2024-05-21T02:12:27.137Z (4 months ago)
Topics: deep-learning, keras, matrix-factorization, r, recommender-system, tensorflow, triplet-loss
Language: R
Homepage:
Size: 12.7 KB
Stars: 46
Watchers: 5
Forks: 10
Open Issues: 0
Metadata Files:
- Readme: README.md
- License: LICENSE

Awesome Lists containing this project

README

        # deep-learning-recipes

[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)

![License: MIT](https://img.shields.io/github/license/nanxstats/deep-learning-recipes.svg)

R implementation for selected machine learning methods with deep learning frameworks (Keras, Tensorflow).

Principles: clean, self-contained, minimal dependency, works with the latest framework versions.

## Contents

- Triplet losses for implicit feedback recommender systems. [[blog post](https://nanx.me/blog/post/triplet-loss-r-keras/)] [[code](triplet-loss-keras)]

- Matrix factorization for binary implicit feedback data. [[blog post](https://nanx.me/blog/post/recsys-binary-implicit-feedback-r-keras/)] [[code](recsys-binary-implicit-keras.R)]

- "Wide and deep" model for regression/classification. [[blog post](https://nanx.me/blog/post/building-my-first-deep-learning-machine/)] [[code](tensorflow-wide-n-deep.R)]