BIG DATA ANALYTICS using MATLAB: NEURAL NETWORKS and APPLICATIONS

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-dataData Science
  • 立即出貨(限量) (庫存=1)

商品描述

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

商品描述(中文翻譯)

大數據分析是指對大量數據進行研究,以揭示其中的隱藏模式、相關性和其他洞察。如今的技術使得我們能夠幾乎立即分析數據並從中獲得答案,而傳統的商業智能解決方案則較為緩慢和低效。大數據分析的一個關鍵工具是神經網絡。MATLAB神經網絡工具箱提供了算法、預訓練模型和應用程序,用於創建、訓練、可視化和模擬淺層和深層神經網絡。您可以進行分類、回歸、聚類、降維、時間序列預測以及動態系統建模和控制。深度學習網絡包括卷積神經網絡(ConvNets,CNNs)和自編碼器,用於圖像分類、回歸和特徵學習。對於小型訓練集,您可以通過使用預訓練的深度網絡進行轉移學習,快速應用深度學習。為了加快對大型數據集的訓練速度,您可以使用並行計算工具箱將計算和數據分佈到桌面上的多核處理器和GPU上,並且可以通過MATLAB分佈式計算服務器擴展到集群和雲端(包括Amazon EC2 P2 GPU實例)。本書開發的主要功能如下:
• 使用卷積神經網絡(用於分類和回歸)和自編碼器(用於特徵學習)進行深度學習
• 使用預訓練的卷積神經網絡模型進行轉移學習
• 在桌面、集群和雲端上使用CPU或多GPU進行訓練和推理
• 無監督學習算法,包括自組織映射和競爭層
• 監督學習算法,包括多層、徑向基、學習向量量化(LVQ)、時滯、非線性自回歸(NARX)和循環神經網絡(RNN)
• 預處理、後處理和網絡可視化,以提高訓練效率和評估網絡性能