NLNS
Neural Large Neighborhood Search for the Capacitated Vehicle Routing Problem (by ahottung)
d2l-en
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge. (by d2l-ai)
| NLNS | d2l-en | |
|---|---|---|
| 1 | 6 | |
| 71 | 21,922 | |
| - | 2.3% | |
| 1.8 | 8.5 | |
| almost 4 years ago | 10 days ago | |
| Python | Python | |
| GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
NLNS
Posts with mentions or reviews of NLNS.
We have used some of these posts to build our list of alternatives
and similar projects.
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[D] What type of machine learning can be used to solve timetable optimisation problems?
I do not believe anyone has tried ML methods for solving timetable problems yet, so this would be new. My group has come up with several different options for ML+Optimization, but probably our approach "Neural Large Neighborhood Search" will be the most promising here. See our ECAI paper: https://ecai2020.eu/papers/786_paper.pdf, medium post explaining the method: https://dot-bielefeld.medium.com/learning-improvement-heuristics-for-vehicle-routing-problems-with-neural-large-neighborhood-search-6e19252e85f4 and source code: https://github.com/ahottung/NLNS.
d2l-en
Posts with mentions or reviews of d2l-en.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-04-10.
- which book to chose for deep learning :lan Goodfellow or francois chollet
- d2l-en: Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge.
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How to pre-train BERT on different objective tasks using HuggingFace
There might is bert library for pre-train bert model in huggingface, But I suggestion that you train bert model in native pytorch to understand detail, Limu's course is recommended for you
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The Transformer in Machine Translation
GitHub's article on Dive into Deep Learning
- D2l-En
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I created a way to learn machine learning through Jupyter
There are actually some online books and courses built on Jupyter Notebook ([Dive to Deep Learning Book](https://github.com/d2l-ai/d2l-en) for example). However yours is more detail and could really helps beginners.