Matlab Neural Network Toolbox Getting Started GuideКНИГИ » ПРОГРАММИНГ
Название: Matlab Neural Network Toolbox Getting Started Guide Автор: Mark Hudson Beale, Martin T. Hagan, Howard B. Demuth Издательство: The MathWorks, Inc Год: 2018 Страниц: 136 Язык: английский Формат: pdf (true) Размер: 10.1 MB
Neural Network Toolbox provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control.
Deep learning networks include convolutional neural networks (ConvNets, CNNs), directed acyclic graph (DAG) network topologies, and autoencoders for image classification, regression, and feature learning. For time-series classification and regression, the toolbox provides long short-term memory (LSTM) deep learning networks.
You can visualize intermediate layers and activations, modify network architecture, and monitor training progress. For small training sets, you can quickly apply deep learning by performing transfer learning with pretrained deep network models (including Inception-v3, ResNet-50, ResNet-101, GoogLeNet, AlexNet, VGG-16, and VGG-19) and models imported from TensorFlow-Keras or Caffe.
To speed up training on large datasets, you can distribute computations and data across multicore processors and GPUs on the desktop (with Parallel Computing ToolboxTM), or scale up to clusters and clouds, including Amazon EC2 P2, P3, and G3 GPU instances (with MATLAB Distributed Computing ServerTM).
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Matlab Neural Network Toolbox Getting Started Guide 
