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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

    [33] [43] In addition to building and training their model, TensorFlow can also help load the data to train the model, and deploy it using TensorFlow Serving. [ 44 ] TensorFlow provides a stable Python Application Program Interface ( API ), [ 45 ] as well as APIs without backwards compatibility guarantee for Javascript , [ 46 ] C++ , [ 47 ] and ...

  4. Horovod (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Horovod_(machine_learning)

    Horovod is a free and open-source software framework for distributed deep learning training using TensorFlow, Keras, PyTorch, and Apache MXNet. Horovod is hosted under the Linux Foundation AI (LF AI). [3] Horovod has the goal of improving the speed, scale, and resource allocation when training a machine learning model. [4]

  5. Neural network (machine learning) - Wikipedia

    en.wikipedia.org/wiki/Neural_network_(machine...

    Choice of model: This depends on the data representation and the application. Model parameters include the number, type, and connectedness of network layers, as well as the size of each and the connection type (full, pooling, etc. ). Overly complex models learn slowly. Learning algorithm: Numerous trade-offs exist between learning algorithms.

  6. Fashion MNIST - Wikipedia

    en.wikipedia.org/wiki/Fashion_MNIST

    The Fashion MNIST dataset is a large freely available database of fashion images that is commonly used for training and testing various machine learning systems. [1] [2] Fashion-MNIST was intended to serve as a replacement for the original MNIST database for benchmarking machine learning algorithms, as it shares the same image size, data format and the structure of training and testing splits.

  7. T5 (language model) - Wikipedia

    en.wikipedia.org/wiki/T5_(language_model)

    It is not a model. [22] The original T5 codebase was implemented in TensorFlow with MeshTF. [2] UL2 20B (2022): a model with the same architecture as the T5 series, but scaled up to 20B, and trained with "mixture of denoisers" objective on the C4. [23] It was trained on a TPU cluster by accident, when a training run was left running ...

  8. Russia will boost AI clout despite West's sanctions, Sberbank ...

    www.aol.com/news/russia-boost-global-ai-clout...

    MOSCOW (Reuters) -Russia has the potential to improve its position in global AI ratings by 2030 despite Western sanctions thanks to talented developers and its own generative AI models, the first ...

  9. Transformer (deep learning architecture) - Wikipedia

    en.wikipedia.org/wiki/Transformer_(deep_learning...

    The transformer model has been implemented in standard deep learning frameworks such as TensorFlow and PyTorch. Transformers is a library produced by Hugging Face that supplies transformer-based architectures and pretrained models.