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)
大數據分析檢視大量數據,以揭示隱藏的模式、相關性和其他見解。憑藉當今的技術,幾乎可以立即分析您的數據並從中獲得答案——這一過程在傳統商業智慧解決方案中則較慢且效率較低。在大數據分析中的一個關鍵工具是神經網絡。MATLAB 神經網絡工具箱提供算法、預訓練模型和應用程式,以創建、訓練、可視化和模擬淺層和深層神經網絡。您可以執行分類、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(Convolutional Neural Networks, ConvNets, CNNs)和自編碼器(autoencoders),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遷移學習來快速應用深度學習。為了加速在大型數據集上的訓練,您可以使用平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,並且可以擴展到集群和雲端(包括 Amazon EC2 P2 GPU 實例)使用 MATLAB 分佈式計算伺服器。本書中開發的主要特點如下:• 使用卷積神經網絡進行深度學習(用於分類和回歸)和自編碼器(用於特徵學習)• 使用預訓練的卷積神經網絡模型進行遷移學習• 在桌面、集群和雲端上使用 CPU 或多 GPU 進行訓練和推斷• 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(Learning Vector Quantization, LVQ)、時間延遲、非線性自回歸(Nonlinear Autoregressive, NARX)和遞迴神經網絡(Recurrent Neural Network, 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。