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Active Learning download for Windows

Free download Active Learning Windows app to run online win Wine in Ubuntu online, Fedora online or Debian online

This is the Windows app named Active Learning whose latest release can be downloaded as active-learningsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.

Download and run online this app named Active Learning with OnWorks for free.

Sundin ang mga tagubiling ito upang patakbuhin ang app na ito:

- 1. Na-download ang application na ito sa iyong PC.

- 2. Ipasok sa aming file manager https://www.onworks.net/myfiles.php?username=XXXXX kasama ang username na gusto mo.

- 3. I-upload ang application na ito sa naturang filemanager.

- 4. Magsimula ng anumang OS OnWorks online emulator mula sa website na ito, ngunit mas mahusay na Windows online emulator.

- 5. Mula sa OnWorks Windows OS na kasisimula mo pa lang, pumunta sa aming file manager https://www.onworks.net/myfiles.php?username=XXXX gamit ang username na gusto mo.

- 6. I-download ang application at i-install ito.

- 7. I-download ang Wine mula sa iyong mga Linux distributions software repository. Kapag na-install na, maaari mong i-double click ang app upang patakbuhin ang mga ito gamit ang Wine. Maaari mo ring subukan ang PlayOnLinux, isang magarbong interface sa ibabaw ng Wine na tutulong sa iyong mag-install ng mga sikat na programa at laro sa Windows.

Ang alak ay isang paraan upang patakbuhin ang software ng Windows sa Linux, ngunit walang kinakailangang Windows. Ang alak ay isang open-source na layer ng compatibility ng Windows na maaaring direktang magpatakbo ng mga program sa Windows sa anumang desktop ng Linux. Sa totoo lang, sinusubukan ng Wine na muling ipatupad ang sapat na Windows mula sa simula upang mapatakbo nito ang lahat ng mga Windows application na iyon nang hindi talaga nangangailangan ng Windows.

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DESCRIPTION

Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. The main experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks.



Mga tampok

  • Modular experimentation framework for active learning research
  • Supports multiple datasets and models including SVMs, logistic regression, and CNNs
  • Implements a variety of active learning strategies such as margin sampling and k-center greedy
  • Allows flexible configuration of parameters such as batch size, warm start ratio, and noise control
  • Easy integration of new models and sampling methods through an extensible API
  • Provides comprehensive benchmarking and analysis tools for experimental comparison


Wika ng Programming

Sawa


Kategorya

Algorithm

This is an application that can also be fetched from https://sourceforge.net/projects/active-learning.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.


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