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Network Architecture1.1 Convolutional Neural Networks (CNNs)CNN is a kind of ...

Network Architecture1.1 Convolutional Neural Networks (CNNs)CNN is a kind of artificial neural network model based on the visual mechanism of the biological brain. It can extract and process the local information of the image through the convolution operation, and then compress and abstract the extracted features through the pooling layer and the fully connected layer, so as to achieve the classification and recognition of the image.1.2 Recurrent Neural Networks (RNNs)RNN is a kind of neural network model that can process sequential data. It can store the historical information of the sequence through the memory cell, and use this information to predict the future behavior of the sequence. RNN is widely used in speech recognition, language translation, etc.1.3 TransformerTransformer is a kind of neural network model that can process sequence data in both directions. It can encode the sequence through the encoder, and use this encoded information to decode the sequence information from left to right and from right to left, and finally merge these two sequences into one sequence to obtain the output. Transformer is widely used in machine translation, text classification, etc.1.4 BERT (Bidirectional Encoder Representations from Transformers)BERT is a self-supervised language representation model based on Transformer. It can achieve better language representation performance than traditional language representation models such as word2vec and GloVe by pre-training on large-scale unlabeled text data. BERT is widely used in various NLP tasks, such as text classification, named entity recognition, language understanding, etc.1.5 GPT series (Generative Pre-trained Transformer)GPT series is a kind of generative dialogue model based on Transformer. It mainly includes GPT-1, GPT-2, GPT-3 and other models. GPT-3 is especially noteworthy, which has achieved human-level language generation ability after being trained on a large-scale corpus. GPT series models are widely used in various tasks such as text generation, dialogue generation, and code generation.Network Training and Optimization2.1 BackpropagationBackpropagation is a method for optimizing neural network parameters by calculating gradient descent based on loss function. It can automatically find out which parameter has a greater impact on loss function by calculating gradient, so as to adjust the parameter value in the direction of reducing loss function.2.2 MomentumMomentum is a method to accelerate gradient descent by considering the historical gradient information when updating parameters. It can reduce the oscillation and local minimum points encountered during gradient descent, so as to obtain a better parameter optimization result.2.3 Adam (Adaptive Moment Estimation)Adam is a kind of parameter optimization algorithm based on Momentum and RMSprop. It can estimate the first moment and second moment of gradient descent through variance reduction technology, and adjust the update rate of each parameter according to the difference between these estimated values and true values. Adam is widely used because of its good convergence performance and stability.2.4 Cyclical Learning RateCyclical Learning Rate is a kind of parameter optimization algorithm that can change the learning rate according to the training progress. It can simulate the "forgetting curve" that human beings have when learning new things, and adjust the learning rate according to this curve, so as to achieve better parameter optimization results.Network Regularization3.1 DropoutDropout is a kind of regularization technology that randomly sets some nodes to zero in each training iteration during network training to prevent overfitting. It can reduce the dependence of the model on some specific nodes, so as to enhance the generalization ability of the model. Dropout is widely used in CNN and RNN models.3.2 Batch Normalization (BN)Batch Normalization is a kind of regularization technology that normalizes the input of each layer in each training batch to reduce internal covariant shift. It can adjust the activation distribution of each layer during training so as to speed up network convergence and reduce overfitting. BN is widely used in CNN and Transformer models.3.3 Weight Decay (L2 Regularization)Weight Decay is a kind of regularization technology that adds an L2 norm penalty term to the loss function to encourage smaller weights in parameter optimization process. It can prevent overfitting by reducing unnecessary parameters and reducing model complexity. Weight Decay is widely used in various neural network models.Network Model Selection and Application Techniques4.1 Transfer LearningTransfer Learning is a kind of machine learning technology that transfers knowledge learned from source tasks to target tasks through pre-training models or fine-