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  2. Keras - Wikipedia

    en.wikipedia.org/wiki/Keras

    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, models, or metrics that can be used in native workflows in JAX, TensorFlow, or PyTorch — with ...

  3. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    TensorFlow 2.0 introduced many changes, the most significant being TensorFlow eager, which changed the automatic differentiation scheme from the static computational graph to the "Define-by-Run" scheme originally made popular by Chainer and later PyTorch. [32]

  4. Open-source artificial intelligence - Wikipedia

    en.wikipedia.org/wiki/Open-source_artificial...

    Open-source artificial intelligence has brought widespread accessibility to machine learning (ML) tools, enabling developers to implement and experiment with ML models across various industries. Sci-kit Learn, Tensorflow, and PyTorch are three of the most widely used open-source ML libraries, each contributing unique capabilities to the field. [58]

  5. Word2vec - Wikipedia

    en.wikipedia.org/wiki/Word2vec

    Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.

  6. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    TensorFlow: Apache 2.0-licensed Theano-like library with support for CPU, GPU and Google's proprietary TPU, [116] mobile; Theano: A deep-learning library for Python with an API largely compatible with the NumPy library. Torch: A scientific computing framework with support for machine learning algorithms, written in C and Lua.

  7. Dask (software) - Wikipedia

    en.wikipedia.org/wiki/Dask_(software)

    Dask-ML is compatible with scikit-learn’s estimator API of fit, transform and predict and is well integrated with machine learning and deep learning frameworks such XGBoost, LightGBM, PyTorch, Keras, and TensorFlow through scikit-learn compatible wrappers.

  8. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  9. Neural Designer - Wikipedia

    en.wikipedia.org/wiki/Neural_Designer

    Neural Designer is a software tool for machine learning based on neural networks, a main area of artificial intelligence research, and contains a graphical user interface which simplifies data entry and interpretation of results.