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During the Google I/O Conference in June 2016, Jeff Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google. [20] In March 2018, Google announced TensorFlow.js version 1.0 for machine learning in JavaScript. [21] In Jan 2019, Google announced TensorFlow 2.0. [22] It became officially available in ...
Keras is an open-source library that provides a Python interface for artificial neural networks. Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers ...
Python: Python: Only on Linux No Yes No Yes Yes Keras: François Chollet 2015 MIT license: Yes Linux, macOS, Windows: Python: Python, R: Only if using Theano as backend Can use Theano, Tensorflow or PlaidML as backends Yes No Yes Yes [20] Yes Yes No [21] Yes [22] Yes MATLAB + Deep Learning Toolbox (formally Neural Network Toolbox) MathWorks ...
[11] along with TensorFlow, Pytorch, XGBoost and 8 other libraries. Kaggle listed CatBoost as one of the most frequently used machine learning (ML) frameworks in the world. It was listed as the top-8 most frequently used ML framework in the 2020 survey [12] and as the top-7 most frequently used ML framework in the 2021 survey. [13]
PyTorch 2.0 was released on 15 March 2023, introducing TorchDynamo, a Python-level compiler that makes code run up to 2x faster, along with significant improvements in training and inference performance across major cloud platforms. [25] [26]
Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.
Name Owner Platforms License; Chromium Embedded Framework (CEF) : CEF Project Page Linux, macOS, Microsoft Windows: Free: BSD CEGUI: CEGUI team Linux, macOS ...
Before version 3.0, Python had two kinds of classes (both using the same syntax): old-style and new-style; [113] current Python versions only support the semantics of the new style. Python supports optional type annotations. [4] [114] These annotations are not enforced by the language, but may be used by external tools such as mypy to catch errors.