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students to help. At the same time, some problems will be summarized, mainly involving Lstm, CNN, Autoencoder, SEQ2SEQ, and computer vision related to the main algorithm (AlexNet, ResNet, vggnet, etc.), Some common functions of tensorflow, Summary of common concepts (such as convolution, pooling, gate, dropout, full connection, activation function, etc.) Also learn a lot of formulas and deep learning related to the paper is complex, so that it is in
not need to train the low level feature every time. This is the benefit of deep. Figure 4: Different level Features-2 When comparing the effect of deep neural network with only one hidden layer neural network, it is necessary to control the number of trainable parameters of two networks, otherwise there is no comparability. In his machine learning video, Professor Li Hongyi, for example, deep performance is better with the same number of parameters, which means that deep parameters will be les
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