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R-CNN architecture Region-based Convolutional Neural Networks (R-CNN) are a family of machine learning models for computer vision , and specifically object detection and localization. [ 1 ] The original goal of R-CNN was to take an input image and produce a set of bounding boxes as output, where each bounding box contains an object and also the ...
Features include mixed precision training, single-GPU, multi-GPU, and multi-node training as well as custom model parallelism. The DeepSpeed source code is licensed under MIT License and available on GitHub. [5] The team claimed to achieve up to a 6.2x throughput improvement, 2.8x faster convergence, and 4.6x less communication. [6]
It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8] Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL . [ 9 ] [ 10 ]
With the release of version 0.3.0 in April 2016 [4] the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", [5] which further increased usage.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [ 1 ]
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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 ...
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