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In addition to standard neural networks, Keras has support for convolutional and recurrent neural networks. It supports other common utility layers like dropout, batch normalization, and pooling. [12] Keras allows users to produce deep models on smartphones (iOS and Android), on the web, or on the Java Virtual Machine. [8]
SqueezeNet is a deep neural network for image classification released in 2016. SqueezeNet was developed by researchers at DeepScale, University of California, Berkeley, and Stanford University. In designing SqueezeNet, the authors' goal was to create a smaller neural network with fewer parameters while achieving competitive accuracy.
A convolutional neural network (CNN) is a regularized type of feed-forward 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]
On April 24, 2024, Huawei's MindSpore 2.3.RC1 was released to open source community with Foundation Model Training, Full-Stack Upgrade of Foundation Model Inference, Static Graph Optimization, IT Features and new MindSpore Elec MT (MindSpore-powered magnetotelluric) Intelligent Inversion Model.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. [4] It is written in C++, with a Python interface. [5]
3.47: December 18, 2014: Densely Connected Convolutional Networks [11] 3.46: August 24, 2016: Shake-Shake regularization [12] 2.86: May 21, 2017: Coupled Ensembles of Neural Networks [13] 2.68: September 18, 2017: ShakeDrop regularization [14] 2.67 Feb 7, 2018 Improved Regularization of Convolutional Neural Networks with Cutout [15] 2.56 Aug 15 ...
Apache MXNet is an open-source deep learning software framework that trains and deploys deep neural networks. It aims to be scalable, allows fast model training , and supports a flexible programming model and multiple programming languages (including C++ , Python , Java , Julia , MATLAB , JavaScript , Go , R , Scala , Perl , and Wolfram Language ).
The network is based on a fully convolutional neural network [2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation. Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture. [1] [3] [4] [5]