Welcome to meta-blocks's documentation!
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**Deployment & Documentation & Stats**
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:alt: PyPI - Python Version
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:alt: Build Status
**Meta-Blocks** is a modular toolbox for research, experimentation, and reproducible benchmarking of learning-to-learn algorithms.
The toolbox provides flexible APIs for working with ``MetaDatasets``, ``TaskDistributions``, and ``MetaLearners`` (see the figure below).
The APIs make it easy to implement a variety of meta-learning algorithms, run them on well-established benchmarks,
or add your own meta-learning problems to the suite and benchmark algorithms on them.
.. image:: _static/img/system_illustration.png
:alt: System Illustration
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**Meta-Blocks** package comes with:
* **Flexible APIs, detailed documentation, and multiple examples.**
* **Popular models and algorithms** such as MAML :cite:`finn2017model`, Reptile :cite:`nichol2018first`, Protonets :cite:`snell2017prototypical`.
* **Supervised and unsupervised meta-learning** setups compatible with all algorithms.
* **Customizable modules and utility functions** for quick prototyping on new meta-learning algorithms.
**Key Links and Resources:**
* `View the latest codes on Github `_
* `Execute Interactive Jupyter Notebooks `_
* `Anomaly Detection Resources `_
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.. toctree::
:maxdepth: 2
:caption: Getting Started
install
examples
.. toctree::
:maxdepth: 2
:caption: Documentation
meta_blocks
.. toctree::
:maxdepth: 2
:hidden:
:caption: Additional Information
about
faq
whats_new
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.. rubric:: References
.. bibliography:: references.bib
:cited:
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Indices and Tables
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* :ref:`genindex`
* :ref:`modindex`
* :ref:`search`