Parrot Trade
  • Parrot Trade
  • Introduction
  • Background of the project
  • Project Overview
  • Technical Overview
  • Technical architecture overview
  • Core functions and features
  • Data collection and preprocessing
  • Data cleaning and denoising
  • Data standardization and normalization
  • Data transformation and feature engineering
  • AI intelligent learning and training model
  • Neural network model architecture
  • Supervised and unsupervised learning algorithms
  • Model evaluation and optimization strategies
  • Intelligent Trading Strategy
  • Risk Management and Money Management
  • Real-time decision-making and trade execution
  • Technical implementation and system architecture
  • Data storage and processing
  • Real-time data stream processing
  • Distributed computing and high availability
  • Security and Privacy Protection
  • Development Roadmap
  • Commercial Application and Value
  • Conclusion and Outlook
  • Appendix
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Neural network model architecture

The architectural design of neural network models plays a key role in AI intelligent learning and training models. The following is an example of parrot's neural network model architecture:

  1. Input Layer:

    • The input layer accepts raw feature data as input. Each input node corresponds to a feature, and the number of nodes in the input layer depends on the dimension of the feature.

  2. Hidden Layers:

    • Hidden layer is the middle layer in a neural network that is used to extract features and learn representations of data. Can contain multiple hidden layers, each containing multiple neurons (nodes).

  3. Activation Functions:

    • The activation function is used to introduce nonlinear transformation and increase the expressive ability of the model. Common activation functions include ReLU, Sigmoid, Tanh, etc.

  4. Output Layer:

    • The output layer produces the output results of the model, which can be class probabilities for classification problems or predicted values for regression problems. The number of nodes in the output layer depends on the type of problem.

  5. Loss Function:

    • The loss function measures the difference between the model output and the true label. For classification problems, commonly used loss functions include the cross-entropy loss function; for regression problems, commonly used loss functions include the mean square error loss function.

  6. Optimizer:

    • The optimizer is used to update the parameters of the neural network to minimize the loss function. Common optimizers include stochastic gradient descent (SGD), Adam, RMSProp, etc.

  7. Regularization:

    • Regularization techniques are used to prevent model overfitting. Common regularization techniques include L1 regularization and L2 regularization.

  8. Batch Normalization:

    • Batch normalization is used to speed up the training process and improve the stability of the model. It alleviates the internal covariate drift problem during the training process by normalizing the input data of each batch.

  9. Dropout：

    • Dropout is a regularization technique used to randomly discard some neurons to prevent the neural network from overfitting.

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Last updated 1 year ago