L. Abell

  • 出版商: W. W. Norton
  • 出版日期: 2017-09-04
  • 售價: $1,147
  • 貴賓價: 9.5$1,090
  • 語言: 英文
  • 頁數: 438
  • 裝訂: Paperback
  • ISBN: 197606760X
  • ISBN-13: 9781976067600
  • 相關分類: Matlab大數據 Big-data資料科學
  • 立即出貨(限量) (庫存=1)


Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today’s technology, it’s possible to analyze your data and get answers from it almost immediately – an effort that’s slower and less efficient with more traditional business intelligence solutions. A key tool in big data analytics are the neural networks. MATLAB 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) and autoencoders for image classification, regression, and feature learning. For small training sets, you can quickly apply deep learning by performing transfer learning with pretrained deep networks. To speed up training on large datasets, you can use Parallel Computing Toolbox to distribute computations and data across multicore processors and GPUs on the desktop, and you can scale up to clusters and clouds (including Amazon EC2 P2 GPU instances) with MATLAB Distributed Computing Server. The Key Features developed in this book are de next: • Deep learning with convolutional neural networks (for classification and regression) and autoencoders (for feature learning) • Transfer learning with pretrained convolutional neural network models • Training and inference with CPUs or multi-GPUs on desktops, clusters, and clouds • Unsupervised learning algorithms, including self-organizing maps and competitive layers • Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) • Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance