文件名称:
Data Mining-Practical Machine Learning Tools and Techniques-4ed-2017
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上传时间: 2017-06-03
详细说明： Deep learning .................................................. 417 10.1 Deep Feedforward Networks ...................................................420 The MNIST Evaluation ........................................................... 421 Losses and Regularizat ion ....................................................... 422 Deep Layered Network Architecture ...................................... 423 Activation Functions................................................................ 424 Backpropagation Revisited...................................................... 426 Computation Graphs and Complex Network Structures ........ 429 Checking Backpropagation Implementations ......................... 430 10.2 Training and Evaluating Deep Networks ................................431 Early Stopping ......................................................................... 431 Validation, Cross-Validation, and Hyperparameter Tuning ... 432 Mini-Batch-Based Stochastic Gradient Descent ..................... 433 Pseudocode for Mini-Batch Based Stochastic Gradient Descent.................................................................................434 Learning Rates and Schedules................................................. 434 Regularization With Priors on Parameters.............................. 435 Dropout .................................................................................... 436 Batch Normalization................................................................ 436 Parameter Initialization............................................................ 436 Unsupervised Pretraining......................................................... 437 Data Augmentation and Synthetic Transformations............... 437 10.3 Convolutional Neural Networks ..............................................437 The ImageNet Evaluation and Very Deep Convolutional Networks ..............................................................................438 From Image Filtering to Learnable Convolutional Layers..... 439 Convolutional Layers and Gradients....................................... 443 Pooling and Subsampling Layers and Gradients .................... 444 Implementation ........................................................................ 445 10.4 Autoencoders............................................................................445 Pretraining Deep Autoencoders With RBMs.......................... 448 Denoising Autoencoders and Layerwise Training.................. 448 Combining Reconstructive and Discriminative Learning....... 449 xii Contents ...展开收缩
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