that connection). Can we do the same with neural networks? Can we optimally prune the network connections, remove extraneous nodes, etc. while retaining the model’s performance? In this chapter we introduce introduce the intuition behind sparsity, different possible methods of picking the connections and nodes to prune, and how to prune a given deep learning model to achieve storage and latency gains with a pruned version. Note that the pruned network has fewer nodes and some retained nodes have fewer connections. Let's do an exercise to convince ourselves that setting parameter values to zero indeed results

Encoder Decoder ConvL STM ???????????? = ???????????? − 1 ???????????? = ???????????? + 1 skip connections Arbitrary Input Size 49 ???????????????????????? ???????????? ????????????0 ???????????? ME SPMC Encoder Conv LSTM ???????????? = ???????????? − 1 ???????????? = ???????????? + 1 skip connections Input size: Fully convolutional Decoder Arbitrary Scale Factors 50 2× 3× 4× Parameter Free SPMC Encoder Conv LSTM ???????????? = ???????????? − 1 ???????????? = ???????????? + 1 skip connections Decoder Arbitrary Temporal Length 51 3 frames 5 frames ???????????????????????? ????????

edia/File:Moving_From_unknown_to_known_feature_spaces_based_on_TS-ELM_with_random_kernels_and_connections.tif https://commons.wikimedia.org/wiki/Category:Machine_learning_algorithms#/media/File:Moving _From_unknown_to_known_feature_spaces_based_on_TS-ELM_with_random_kernels_and_connections.tif https://commons.wikimedia.org/wiki/Category:Machine_learning_algorithms#/media/File:OPTICS.svg May be re-distributed

weights while the network is being trained. Once the training concludes, the network has fewer connections which helps in reducing the network size and improving the latency. There are many criteria for process. On the left is the unpruned graph, and on the right is a pruned graph with the unimportant connections and neurons removed. Learning Techniques Learning techniques are training phase techniques to

feedforward network • It can be extended to recurrent neural networks (RNN) by involving feedback connections, which power many natural language applications 2 / 19 Neuron 3 / 19 Neuron (Contd.) • Neuron

classification  MNIST database of handwritten digits  Fashion-MNIST database of fashion articles  Boston housing price regression dataset Let us use the MNIST database of handwritten digits (or Weights. Model weights are large file so we have to download and extract the feature from ImageNet database. Some of the popular pre-trained models are listed below,  ResNet  VGG16  MobileNet

subsequent version of this controller added additional parameters for each layer to allow skip connections. Figure 7-6: The architecture of a controller which predicts convolutional networks. Each timestep

are not accounting for activation functions like tanh which further worsen the problem. Skip connections as introduced in the ResNet architecture is one step towards solving this problem by creating ‘residual

itself Example 2 T: Recognizing hand-written words P: Percentage of words correctly classified E: Database of human-labeled images of handwritten words Feng Li (SDU) Overview September 6, 2023 10 / 57 Categorize email messages as spam or legitimate P: Percentage of email messages correctly classified E: Database of emails, some with human-given labels Example 4 T: Driving on four-lane highways using vision

Model 训练模型 保存和加载 h5 模型 保存和加载 SavedModel 模型 Fashion MNIST 数据集介绍 Original MNIST dataset The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and