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awesome-machine-learning | curated list of awesome Machine Learning | Machine Learning library

 by   josephmisiti Python Version: Current License: Non-SPDX

 by   josephmisiti Python Version: Current License: Non-SPDX

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kandi X-RAY | awesome-machine-learning Summary

awesome-machine-learning is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Machine Learning applications. awesome-machine-learning has no bugs, it has no vulnerabilities and it has medium support. However awesome-machine-learning build file is not available and it has a Non-SPDX License. You can download it from GitHub.
A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.
Support
Support
Quality
Quality
Security
Security
License
License
Reuse
Reuse

kandi-support Support

  • awesome-machine-learning has a medium active ecosystem.
  • It has 51223 star(s) with 12681 fork(s). There are 3444 watchers for this library.
  • It had no major release in the last 12 months.
  • There are 1 open issues and 70 have been closed. On average issues are closed in 51 days. There are 2 open pull requests and 0 closed requests.
  • It has a neutral sentiment in the developer community.
  • The latest version of awesome-machine-learning is current.
This Library - Support
Best in #Machine Learning
Average in #Machine Learning
This Library - Support
Best in #Machine Learning
Average in #Machine Learning

quality kandi Quality

  • awesome-machine-learning has 0 bugs and 0 code smells.
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning
This Library - Quality
Best in #Machine Learning
Average in #Machine Learning

securitySecurity

  • awesome-machine-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • awesome-machine-learning code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
This Library - Security
Best in #Machine Learning
Average in #Machine Learning
This Library - Security
Best in #Machine Learning
Average in #Machine Learning

license License

  • awesome-machine-learning has a Non-SPDX License.
  • Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.
This Library - License
Best in #Machine Learning
Average in #Machine Learning
This Library - License
Best in #Machine Learning
Average in #Machine Learning

buildReuse

  • awesome-machine-learning releases are not available. You will need to build from source code and install.
  • awesome-machine-learning has no build file. You will be need to create the build yourself to build the component from source.
  • awesome-machine-learning saves you 6 person hours of effort in developing the same functionality from scratch.
  • It has 18 lines of code, 0 functions and 1 files.
  • It has low code complexity. Code complexity directly impacts maintainability of the code.
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
This Library - Reuse
Best in #Machine Learning
Average in #Machine Learning
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awesome-machine-learning Key Features

Repository's owner explicitly says that "this library is not maintained".

Not committed for a long time (2~3 years).

For a list of free machine learning books available for download, go here.

For a list of professional machine learning events, go here.

For a list of (mostly) free machine learning courses available online, go here.

For a list of blogs and newsletters on data science and machine learning, go here.

For a list of free-to-attend meetups and local events, go here.

Community Discussions

Trending Discussions on awesome-machine-learning
  • What kind of feature extractor is used in vowpal wabbit?
Trending Discussions on awesome-machine-learning

QUESTION

What kind of feature extractor is used in vowpal wabbit?

Asked 2017-Mar-31 at 23:20

In sklearn when we pass sentence to algorithms we can use text features extractors like the countvectorizer, tf-idf vectoriser etc... And we get an array of floats.

But what we get when passed to vowpal wabbit the input file like this one:

-1 |Words The sun is blue
1 |Words The sun is yellow

What is used in internal implementation of vowpal wabbit? How does this text transform?

ANSWER

Answered 2017-Mar-30 at 21:47

There are two separate questions here:

Q1: Why can't you (and shouldn't you) use transformations like tf-idf when using vowpal wabbit ?

A1: vowpal wabbit is not a batch learning system, it is an online-learning system. In order to compute measures like tf-idf (term frequency in each document vs the whole corpus) you need to see all the data (corpus) first, and sometimes do multiple passes over the data. vowpal wabbit as an online/incremental learning system is designed to also work on problems where you don't have the full data ahead of time. See This answer for a lot more details.

Q2: How does vowpal wabbit "transform" the features it sees ?

A2: It doesn't. It simply maps each word feature on-the-fly to its hashed location in memory. The online learning step is driven by a repetitive optimization loop (SGD or BFGS) example by example, to minimize the modeling error. You may select the loss function to optimize for.

However, if you already have the full data you want to train on, nothing prevents you from transforming it (using any other tool) before feeding the transformed values to vowpal wabbit. It's your choice. Depending on the particular data, you may get better or worse results using a transformation pre-pass, than by running multiple passes with vowpal wabbit itself without preliminary transformations (check-out the vw --passes option).

To complete the answer, let's add another related question:

Q3: Can I use pre-transformed (e.g. tf-idf) data with vowpal wabbit ?

A3: Yes, you can. Just use the following (post-transformation) form. Instead of words, use integers as feature IDs and since any feature can have an optional explicit weight, use the tf-idf floating point as weights, following the : separator in typical SVMlight format:

-1 |  1:0.534  15:0.123  3:0.27  29:0.066  ...
1  |  3:0.1  102:0.004  24:0.0304  ...

The reason this works, is because vw has a nice feature of distinguishing between string and integer-features. It doesn't hash feature-names that look like integers (unless you use the --hash_all option explicitly). Integer feature numbers are used directly as if they were the hash result of the feature.

Source https://stackoverflow.com/questions/43097894

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install awesome-machine-learning

You can download it from GitHub.
You can use awesome-machine-learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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