Welcome to meta-blocks's documentation! ======================================= **Deployment & Documentation & Stats** .. image:: https://img.shields.io/pypi/pyversions/meta-blocks :target: https://pypi.org/project/meta-blocks/ :alt: PyPI - Python Version .. image:: https://badge.fury.io/py/meta-blocks.svg :target: https://pypi.org/project/meta-blocks/ :alt: PyPI Version .. image:: https://travis-ci.org/alshedivat/meta-blocks.svg :target: https://travis-ci.org/alshedivat/meta-blocks :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 ---- **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 `_ ---- .. 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 ---- .. rubric:: References .. bibliography:: references.bib :cited: ---- Indices and Tables ------------------ * :ref:`genindex` * :ref:`modindex` * :ref:`search`