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  2. Chainer - Wikipedia

    en.wikipedia.org/wiki/Chainer

    Chainer is an open source deep learning framework written purely in Python on top of NumPy and CuPy Python libraries. The development is led by Japanese venture company Preferred Networks in partnership with IBM, Intel, Microsoft, and Nvidia.

  3. TensorFlow - Wikipedia

    en.wikipedia.org/wiki/TensorFlow

    TensorFlow.nn is a module for executing primitive neural network operations on models. [ 38] Some of these operations include variations of convolutions (1/2/3D, Atrous, depthwise), activation functions ( Softmax, RELU, GELU, Sigmoid, etc.) and their variations, and other operations ( max-pooling, bias-add, etc.).

  4. Transformer (deep learning architecture) - Wikipedia

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

    Machine learningand data mining. A standard Transformer architecture, showing on the left an encoder, and on the right a decoder. Note: it uses the pre-LN convention, which is different from the post-LN convention used in the original 2017 Transformer. A transformer is a deep learning architecture developed by researchers at Google and based on ...

  5. PyTorch - Wikipedia

    en.wikipedia.org/wiki/PyTorch

    Although the Python interface is more polished and the primary focus of development, PyTorch also has a C++ interface. [ 12 ] A number of pieces of deep learning software are built on top of PyTorch, including Tesla Autopilot , [ 13 ] Uber 's Pyro, [ 14 ] Hugging Face 's Transformers, [ 15 ] PyTorch Lightning , [ 16 ] [ 17 ] and Catalyst.

  6. 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 ...

  7. Deep learning - Wikipedia

    en.wikipedia.org/wiki/Deep_learning

    Deep learning is the subset of machine learning methods based on neural networks with representation learning. The adjective "deep" refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised. [ 2]

  8. Comparison of deep learning software - Wikipedia

    en.wikipedia.org/wiki/Comparison_of_deep...

    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 ...

  9. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    Residual neural network. A Residual Block in a deep Residual Network. Here the Residual Connection skips two layers. A residual neural network (also referred to as a residual network or ResNet) [ 1] is a deep learning architecture in which the weight layers learn residual functions with reference to the layer inputs.