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

 by   josephmisiti Python Version: Current License: Non-SPDX

kandi X-RAY | awesome-machine-learning Summary

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.

            kandi-support Support

              awesome-machine-learning has a medium active ecosystem.
              It has 59138 star(s) with 14069 fork(s). There are 3331 watchers for this library.
              It had no major release in the last 6 months.
              There are 1 open issues and 82 have been closed. On average issues are closed in 18 days. There are 1 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.

            kandi-Quality Quality

              awesome-machine-learning has 0 bugs and 0 code smells.

            kandi-Security Security

              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.

            kandi-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.

            kandi-Reuse Reuse

              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.

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            awesome-machine-learning Key Features

            No Key Features are available at this moment for awesome-machine-learning.

            awesome-machine-learning Examples and Code Snippets

            No Code Snippets are available at this moment for awesome-machine-learning.

            Community Discussions

            Trending Discussions on awesome-machine-learning


            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:



            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:


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


            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.


            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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